Rozprawy doktorskie: „Task assignment”

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Manoharan, Sathiamoorthy. "Task assignment in parallel processor systems." Thesis, University of Edinburgh, 1993. http://hdl.handle.net/1842/6568.

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A generic object-oriented simulation platform is developed in order to conduct experiments on the performance of assignment schemes. The simulation platform, called Genesis, is generic in the sense that it can model the key parameters that describe a parallel system: the architecture, the program, the assignment scheme and the message routing strategy. Genesis uses as its basis a sound architectural representation scheme developed in the thesis. The thesis reports results from a number of experiments assessing the performance of assignment schemes using Genesis. The comparison results indicate that the new assignment scheme proposed in this thesis is a promising alternative to the work-greedy assignment schemes. The proposed scheme has a time-complexity less than those of the work-greedy schemes and achieves an average performance better than, or comparable to, those of the work-greedy schemes. To generate an assignment, some parameters describing the program model will be required. In many cases, accurate estimation of these parameters is hard. It is thought that inaccuracies in the estimation would lead to poor assignments. The thesis investigates this speculation and presents experimental evidence that shows such inaccuracies do not greatly affect the quality of the assignments.

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Monori, Akos. "Task assignment optimization in SAP Extended WarehouseManagement." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3598.

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Nowadays in the world of mass consumption there is big demand for distributioncenters of bigger size. Managing such a center is a very complex and difficult taskregarding to the different processes and factors in a usual warehouse when we want tominimize the labor costs. Most of the workers’ working time is spent with travelingbetween source and destination points which cause deadheading. Even if a worker knowsthe structure of a warehouse well and because of that he or she can find the shortest pathbetween two points, it is still not guaranteed that there won’t be long traveling timebetween the locations of two consecutive tasks. We need optimal assignments betweentasks and workers.In the scientific literature Generalized Assignment Problem (GAP) is a wellknownproblem which deals with the assignment of m workers to n tasks consideringseveral constraints. The primary purpose of my thesis project was to choose a heuristics(genetic algorithm, tabu search or ant colony optimization) to be implemented into SAPExtended Warehouse Management (SAP EWM) by with task assignment will be moreeffective between tasks and resources.After system analysis I had to realize that due different constraints and businessdemands only 1:1 assingments are allowed in SAP EWM. Because of that I had to use adifferent and simpler approach – instead of the introduced heuristics – which could gainbetter assignments during the test phase in several cases. In the thesis I described indetails what ware the most important questions and problems which emerged during theplanning of my optimized assignment method.

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Brunet, Luc (Luc P. V. ). "Consensus-based auctions for decentralized task assignment." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44926.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Includes bibliographical references (p. 137-147). This thesis addresses the decentralized task assignment problem in cooperative autonomous search and track missions by presenting the Consensus-Based class of assignment algorithms. These algorithm make use of information consensus routines to converge on the assignment rather than the situational awareness of the fleet. A market-based approach is used as the mechanism for task selection, while the novel consensus stage of the algorithms allow for fast distributed conflict resolution. Three separate algorithms belonging to the Consensus-Based class of assignment strategies will be presented. The first is the Consensus-Based Auction Algorithm (CBAA), which is a single assignment auction strategy that is shown to be bounded within 50% of the optimal solution, while an upper-bound on convergence is presented. Two multi-assignment algorithms are then presented as extensions of the CBAA. The iterative CBAA executes the single assignment algorithm multiple times in order to build an assignment with multiple tasks. The second algorithm is the more general Consensus-Based Bundle Algorithm (CBBA) in which agents build a candidate bundle of tasks and bid on each task individually based on the improvement in score achieved by adding it to the bundle. Both algorithms are shown to be lower bounded by 50% optimality, while convergence bounds are derived based on the network topology. Numerical results show that the bundle algorithm performs much better than the iterative approach while providing faster convergence times. It is also compared with the Prim Allocation (PA) auction algorithm where it is shown to exhibit much faster convergence times and give better assignments. The CBBA is also implemented in the CSAT simulation test-bed developed by Aurora Flight Sciences in conjunction with MIT, and shown to produce faster response times and better tracking performance than the currently used RDTA algorithm. by Luc Brunet. S.M.

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Lavoie, Marco Carleton University Dissertation Information and Systems Science. "Task assignment in a DSP multiprocessor environment." Ottawa, 1990.

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Alighanbari, Mehdi 1976. "Robust and decentralized task assignment algorithms for UAVs." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42177.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007. Includes bibliographical references (p. 149-158). This thesis investigates the problem of decentralized task assignment for a fleet of UAVs. The main objectives of this work are to improve the robustness to noise and uncertainties in the environment and improve the scalability of standard centralized planning systems, which are typically not practical for large teams. The main contributions of the thesis are in three areas related to distributed planning: information consensus, decentralized conflict-free assignment, and robust assignment. Information sharing is a vital part of many decentralized planning algorithms. A previously proposed decentralized consensus algorithm uses the well-known Kalman filtering approach to develop the Kalman Consensus Algorithm (KCA), which incorporates the certainty of each agent about its information in the update procedure. It is shown in this thesis that although this algorithm converges for general form of network structures, the desired consensus value is only achieved for very special networks. We then present an extension of the KCA and show, with numerical examples and analytical proofs, that this new algorithm converges to the desired consensus value for very general communication networks. Two decentralized task assignment algorithms are presented that can be used to achieve a good performance for a wide range of communication networks. These include the Robust Decentralized Task Assignment (RDTA) algorithm, which is shown to be robust to inconsistency of information across the team and ensures that the resulting decentralized plan is conflict-free. A new auction-based task assignment algorithm is also developed to perform assignment in a completely decentralized manner where each UAV is only allowed to communicate with its neighboring UAVs, and there is no relaying of information. (cont.) In this algorithm, only necessary information is communicated, which makes this method communication-efficient and well-suited for low bandwidth communication networks. The thesis also presents a technique that improves the robustness of the UAV task assignment algorithm to sensor noise and uncertainty about the environment. Previous work has demonstrated that an extended version of a simple robustness algorithm in the literature is as effective as more complex techniques, but significantly easier to implement, and thus is well suited for real-time implementation. We have also developed a Filter-Embedded Task assignment (FETA) algorithm for accounting for changes in situational awareness during replanning. Our approach to mitigate "churning" is unique in that the coefficient weights that penalize changes in the assignment are tuned online based on previous plan changes. This enables the planner to explicitly show filtering properties and to reject noise with desired frequencies. This thesis synergistically combines the robust and adaptive approaches to develop a fully integrated solution to the UAV task planning problem. The resulting algorithm, called the Robust Filter Embedded Task Assignment (RFETA), is shown to hedge against the uncertainty in the optimization data and to mitigate the effect of churning while replanning with new information. The algorithm demonstrates the desired robustness and filtering behavior, which yields superior performance to using robustness or FETA alone, and is well suited for real-time implementation. The algorithms and theorems developed in this thesis address important aspects of the UAV task assignment problem. The proposed algorithms demonstrate improved performance and robustness when compared with benchmarks and they take us much closer to the point where they are ready to be transitioned to real missions. by Mehdi Alighanbari. Ph.D.

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Tovstokorenko, O. Y., R. O. Gamzayev, and M. V. Tkachuk. "Case-Based Recommender for intelligent task assignment in software development." Thesis, NTU "KhPI", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/38049.

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Luo, Lingzhi. "Distributed Algorithm Design for Constrained Multi-robot Task Assignment." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/426.

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The task assignment problem is one of the fundamental combinatorial optimization problems. It has been extensively studied in operation research, management science, computer science and robotics. Task assignment problems arise in various applications of multi-robot systems (MRS), such as environmental monitoring, disaster response, extraterrestrial exploration, sensing data collection and collaborative autonomous manufacturing. In these MRS applications, there are realistic constraints on robots and tasks that must be taken into account both from the modeling perspective and the algorithmic perspective. From the modeling aspect, such constraints include (a) Task group constraints: where tasks form disjoint groups and each robot can be assigned to at most one task in each group. One example of the group constraints comes from tightly-coupled tasks, where multiple micro tasks form one tightly-coupled macro task and need multiple robots to perform each simultaneously. (b) Task deadline constraints: where tasks must be assigned to meet their deadlines. (c) Dynamically-arising tasks: where tasks arrive dynamically and the payoffs of future tasks are unknown. Such tasks arise in scenarios like searchrescue, where new victims are found dynamically. (d) Robot budget constraints: where the number of tasks each robot can perform is bounded according to the resource it possesses (e.g., energy). From the solution aspect, there is often a need for decentralized solution that are implemented on individual robots, especially when no powerful centralized controller exists or when the system needs to avoid single-point failure or be adaptive to environmental changes. Most existing algorithms either do not consider the above constraints in problem modeling, are centralized or do not provide formal performance guarantees. In this thesis, I propose methods to address these issues for two classes of problems, namely, the constrained linear assignment problem and constrained generalized assignment problem. Constrained linear assignment problem belongs to P, while constrained generalized assignment problem is NP-hard. I develop decomposition-based distributed auction algorithms with performance guarantees for both problem classes. The multi-robot assignment problem is decomposed into an optimization problem for each robot and each robot iteratively solving its own optimization problem leads to a provably good solution to the overall problem. For constrained linear assignment problem, my approaches provides an almost optimal solution. For constrained generalized assignment problem, I present a distributed algorithm that provides a solution within a constant factor of the optimal solution. I also study the online version of the task allocation problem with task group constraints. For the online problem, I prove that a repeated greedy version of my algorithm gives solution with constant factor competitive ratio. I include simulation results to evaluate the average-case performance of the proposed algorithms. I also include results on multi-robot cooperative package transport to illustrate the approach.

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Ottensmeyer, Mark Peter. "Telerobotic surgery : feedback time delay effects on task assignment." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10972.

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Holmes, Carol Anne. "Equipment selection and task assignment for multiproduct assembly system design." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14936.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 1987. MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY. Bibliography: leaves 81-82. by Carol Anne Holmes. Ph.D.

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Alighanbari, Mehdi 1976. "Task assignment algorithms for teams of UAVs in dynamic environments." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/17754.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; and, (S.M.)--Massachusetts Institute of Technology, Operations Research Center, 2004. Includes bibliographical references (p. 113-118). For many vehicles, obstacles, and targets, coordination of a fleet of Unmanned Aerial Vehicles (UAVs) is a very complicated optimization problem, and the computation time typically increases very rapidly with the problem size. Previous research proposed an approach to decompose this large problem into task assignment and trajectory problems, while capturing key features of the coupling between them. This enabled the control architecture to solve an assignment problem first to determine a sequence of waypoints for each vehicle to visit, and then concentrate on designing paths to visit these pre-assigned waypoints. Although this approach greatly simplifies the problem, the task assignment optimization was still too slow for real-time UAV operations. This thesis presents a new approach to the task assignment problem that is much better suited for replanning in a dynamic battlefield. The approach, called the Receding Horizon Task Assignment (RHTA) algorithm, is shown to achieve near-optimal performance with computational times that are feasible for real-time implementation. Further, this thesis extends the RHTA algorithm to account for the risk, noise, and uncertainty typically associated with the UAV environment. This work also provides new insights on the distinction between UAV coordination and cooperation. The benefits of these improvements to the UAV task assignment algorithms are demonstrated in several simulations and on two hardware platforms. by Mehdi Alighanbari. S.M.

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Samanthula, Krishna Nagarjun Reddy. "A service to automate the task assignment process in YAWL." Kansas State University, 2010. http://hdl.handle.net/2097/4647.

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Master of Science Department of Computing and Information Sciences Gurdip Singh Developing an optimal working environment and managing the of work load in an efficient manner are the major challenges for most businesses today. So, the importance of the workflow and workflow management in an organization is unquestionable. Many organizations use sophisticated systems to organize the workflows. One such workflow system based on a concise and powerful modeling language called “Yet Another Workflow Language” is YAWL. YAWL handles complex data, transformations, integration with organizational resources and Web Service integration. Workflow comprises of three main perspectives: control-flow, data and the resources. In Yawl, the control-flow and the data-flow are tightly coupled within the workflow enactment engine. But the resource perspective is provided by a discrete custom service called Resource Service. Administrative tools are provided using which the administrator has to manually select the resource (referred as participant) which needs to perform a particular task of the workflow. This project aims at developing a service which can automate the assignment of the tasks to the participants by using the Resource service which provides number of interfaces that expose the full functionality of the service. The application of this project with respect to Healthcare domain is presented. Healthcare domain is the one of the most demanding and yet critical business process. Hospitals face increasing pressure to both improve the quality of the services delivered to patients and to reduce costs .Hence there is significant demand on hospitals in regard to how the organization, execution, and monitoring of work processes is performed. Workflow Management Systems like YAWL offers a potential solution as they support processes by managing the flow of work.

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Kao, Yi-Hsuan. "Optimizing task assignment for collaborative computing over heterogeneous network devices." Thesis, University of Southern California, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10124490.

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The Internet of Things promises to enable a wide range of new applications involving sensors, embedded devices and mobile devices. Different from traditional cloud computing, where the centralized and powerful servers offer high quality computing service, in the era of the Internet of Things, there are abundant computational resources distributed over the network. These devices are not as powerful as servers, but are easier to access with faster setup and short-range communication. However, because of energy, computation, and bandwidth constraints on smart things and other edge devices, it will be imperative to collaboratively run a computational-intensive application that a single device cannot support individually. As many IoT applications, like data processing, can be divided into multiple tasks, we study the problem of assigning such tasks to multiple devices taking into account their abilities and the costs, and latencies associated with both task computation and data communication over the network. A system that leverages collaborative computing over the network faces highly variant run-time environment. For example, the resource released by a device may suddenly decrease due to the change of states on local processes, or the channel quality may degrade due to mobility. Hence, such a system has to learn the available resources, be aware of changes and flexibly adapt task assignment strategy that efficiently makes use of these resources. We take a step by step approach to achieve these goals. First, we assume that the amount of resources are deterministic and known. We formulate a task assignment problem that aims to minimize the application latency (system response time) subject to a single cost constraint so that we will not overuse the available resource. Second, we consider that each device has its own cost budget and our new multi-constrained formulation clearly attributes the cost to each device separately. Moving a step further, we assume that the amount of resources are stochastic processes with known distributions, and solve a stochastic optimization with a strong QoS constraint. That is, instead of providing a guarantee on the average latency, our task assignment strategy gives a guarantee that p% of time the latency is less than t, where p and t are arbitrary numbers. Finally, we assume that the amount of run-time resources are unknown and stochastic, and design online algorithms that learn the unknown information within limited amount of time and make competitive task assignment. We aim to develop algorithms that efficiently make decisions at run-time. That is, the computational complexity should be as light as possible so that running the algorithm does not incur considerable overhead. For optimizations based on known resource profile, we show these problems are NP-hard and propose polynomial-time approximation algorithms with performance guarantee, where the performance loss caused by sub-optimal strategy is bounded. For online learning formulations, we propose light algorithms for both stationary environment and non-stationary environment and show their competitiveness by comparing the performance with the optimal offline policy (solved by assuming the resource profile is known). We perform comprehensive numerical evaluations, including simulations based on trace data measured at application run-time, and validate our analysis on algorithm's complexity and performance based on the numerical results. Especially, we compare our algorithms with the existing heuristics and show that in some cases the performance loss given by the heuristic is considerable due to the sub-optimal strategy. Hence, we conclude that to efficiently leverage the distributed computational resource over the network, it is essential to formulate a sophisticated optimization problem that well captures the practical scenarios, and provide an algorithm that is light in complexity and suggests a good assignment strategy with performance guarantee.

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Basik, Kevin J. "Small-Group Leader Assignment: Effects Across Different Degrees of Task Interdependence." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/36882.

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The use of teams and work groups in organizations has become increasingly more popular in the last decade. Within each of these groups, a leadership role must be filled in order for the team to achieve its task. This study posited that the method by which the leader comes into this role may have a direct impact on the group's performance and its perceptions of the group's interpersonal processes and efficiency, satisfaction with the group, satisfaction with the group output, and support for the leader. In addition, the influence of leader assignment was expected to change based on the level of interdependence required by the task. One hundred forty-eight subjects were assigned to one of four conditions in a 2X2 design (appointed vs. elected leaders X high vs. low interdependence task), and were asked to fill out a questionnaire upon completion of their task. Results found that the higher level of interdependence was significantly related to more favorable ratings of Perceived Group Efficiency ( F =6.89, p <.05) and Satisfaction with Group Output ( F =7.69, p <.05). Possible limitations and future research opportunities are addressed. Master of Science

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Broberg, James Andrew, and james@broberg com au. "Effective task assignment strategies for distributed systems under highly variable workloads." RMIT University. Computer Science and Information Technology, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080130.150130.

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Heavy-tailed workload distributions are commonly experienced in many areas of distributed computing. Such workloads are highly variable, where a small number of very large tasks make up a large proportion of the workload, making the load very hard to distribute effectively. Traditional task assignment policies are ineffective under these conditions as they were formulated based on the assumption of an exponentially distributed workload. Size-based task assignment policies have been proposed to handle heavy-tailed workloads, but their applications are limited by their static nature and assumption of prior knowledge of a task's service requirement. This thesis analyses existing approaches to load distribution under heavy-tailed workloads, and presents a new generalised task assignment policy that significantly improves performance for many distributed applications, by intelligently addressing the negative effects on performance that highly variable workloads cause. Many problems associated with the modelling and optimisations of systems under highly variable workloads were then addressed by a novel technique that approximated these workloads with simpler mathematical representations, without losing any of their pertinent original properties. Finally, we obtain advance queuing metrics (such as the variance of key measurements like waiting time and slowdown that are difficult to obtain analytically) through rigorous simulation.

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Kavala, Syam. "Obstacle adaptive approaches for distributed task assignment in autonomous mobile robots." Thesis, Wichita State University, 2009. http://hdl.handle.net/10057/2539.

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It has many challenges to assign a group of mobile robots to individual targets according to the specific constraints. In addition to the group behavior constraints (one-toone or one-to-many) of the task assignment, some of the performance constraints include (1) proximity from robot to target (2) suitability of robot in performing a task and (3) quality of connectivity among the robots. Due to the computational complexities and the nature of the dynamical systems, the task assignment approaches have been developed as distributed and dynamical systems approaches with various simplified assumptions. In this thesis, first, I investigate one of the most recently proposed distributed task assignment approaches (Peter Molnar's approach) that combines target assignment and motion planning in order to minimize the robots travelling distance and overhead cost for its one-to-one target assignment. Second, I find that the approach does not provide an efficient path finding algorithm in the environment with obstacles. It simply uses proximity sensors to direct the robots away from obstacles. Based upon the observation, third, I propose efficient task assignment approaches to minimize the robot's travel distance and overhead cost in an environment with one immobile obstacle of any shape and size. In detail, the thesis addresses 1) obstacle modeling and simplification: Initially the vertices of an obstacle were provided to the robots. The robots reconstruct the obstacle to a rectangle shape that encircles the original obstacle, 2) obstacle decomposition for an adjustment: The reconstructed obstacle is further decomposed in case if there was a robot or target object present within the reconstructed area, and 3) optimal target path calculation: Two approaches are designed for calculating distance from robot to target by taking the reconstructed obstacle into account. Approach 1 calculates the shortest distance from robot to target along the perimeter of the obstacle. Approach 2 further optimizes the path by connecting the robot and target to the shortest distance vertices of the obstacle. The computational overhead and task assignment efficiency of the proposed approaches are compared via MATLAB simulations. Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science

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De, Mel Geeth R. "Intelligent resource selection for sensor-task assignment : a knowledge-based approach." Thesis, University of Aberdeen, 2014. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=215104.

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Today, sensing resources play a crucial role in the success of critical tasks such as border monitoring and surveillance. Although there are various types of resources available, each with different capabilities, only a subset of these resources are useful for a specific task. This is due to the dynamism in tasks' environment and the heterogeneity of the resources. Thus, an effective mechanism to select resources for tasks is needed so that the selected resources cater for the needs of the tasks. Though a considerable amount of research has already been done in different communities to efficiently allocate resources to tasks, we argue that there is little work done to guarantee the effectiveness of the section with respect to the context of operation. In this thesis, we propose a knowledge-based approach in which the context of operation is introduced to the resource selection process. First, we present a formalism to represent a sensor domain. We then introduce sound and complete mechanisms through which effective resource solutions for tasks are discovered. An extension to the representation is then proposed so that the agility in resource selection is increased. Finally, we present an architecture whereby a multitude of such knowledge bases are exposed as services so that a coalition can fully benefit from its networked resources; a query language – and its semantics – to discover appropriate service collections for user requirements are also presented. We have evaluated our work through controlled experiments and critical arguments. Through these evaluations, we have shown that our approach can indeed improve the resource selection process and can augment resource allocation mechanisms. Our approach is general in that, it can be applied in many other domains.

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Walker, David Hunt. "Coordinated UAV target assignment using distributed calculation of target-task tours /." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd400.pdf.

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Walker, David H. "Coordinated UAV Target Assignment Using Distributed Calculation of Target-Task Tours." BYU ScholarsArchive, 2004. https://scholarsarchive.byu.edu/etd/130.

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This thesis addresses the improvement of cooperative task allocation to vehicles in multiple-vehicle, multiple-target scenarios through the use of more effective preplanned tours. Effective allocation of vehicles to targets requires knowledge of both the team objectives and the contributions that individual vehicles can make toward accomplishing team goals. This is primarily an issue of individual vehicle path planning --- determining the path the vehicles will follow to accomplish individual and team goals. Conventional methods plan optimal point-to-point path segments that often result in lengthy and suboptimal tours because the trajectory neither considers future tasks nor the overall path. However, cooperation between agents is improved when the team selects vehicle assignments based on the ability to complete immediate and subsequent tasks. This research demonstrates that planning more efficient tour paths through multiple targets results in better use of individual vehicle resources, faster completion of team objectives, and improved overall cooperation between agents. This research presents a method of assigning unmanned aerial vehicles to targets to improve cooperation. A tour path planning method was developed to overcome shortcomings of traditional point-to-point path planners, and is extended to the specific tour-planning needs of this problem. The planner utilizes an on-line learning heuristic search to find paths that accomplish team goals in the shortest flight time. The learning search planner uses the entire sensor footprint, more efficiently plans tours through closely packed targets, and learns the best order for completion of the multiple tasks. The improved planner results in assignment completion times that range on average between 1.67 and 2.41 times faster, depending on target spread. Assignments created from preplanned tours make better use of vehicle resources and improve team cooperation. Path planning and assignment selection are accomplished in near real-time through the use of path heuristics and assignment cost estimates to reduce the problem size to tractable levels. Assignments are ordered according to estimated or predicted value. A reduced number of ordered assignments is considered and evaluated to control problem growth. The estimates adequately represent the actual assignment value, effectively reduce problem size, and produce near-optimal paths and assignments for near-real-time applications.

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Präntare, Fredrik. "Simultaneous coalition formation and task assignment in a real-time strategy game." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139210.

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In this thesis we present an algorithm that is designed to improve the collaborative capabilities of agents that operate in real-time multi-agent systems. Furthermore, we study the coalition formation and task assignment problems in the context of real-time strategy games. More specifically, we design and present a novel anytime algorithm for multi-agent cooperation that efficiently solves the simultaneous coalition formation and assignment problem, in which disjoint coalitions are formed and assigned to independent tasks simultaneously. This problem, that we denote the problem of collaboration formation, is a combinatorial optimization problem that has many real-world applications, including assigning disjoint groups of workers to regions or tasks, and forming cross-functional teams aimed at solving specific problems. The algorithm's performance is evaluated using randomized artificial problems sets of varying complexity and distribution, and also using Europa Universalis 4 – a commercial strategy game in which agents need to cooperate in order to effectively achieve their goals. The agents in such games are expected to decide on actions in real-time, and it is a difficult task to coordinate them. Our algorithm, however, solves the coordination problem in a structured manner. The results from the artificial problem sets demonstrates that our algorithm efficiently solves the problem of collaboration formation, and does so by automatically discarding suboptimal parts of the search space. For instance, in the easiest artificial problem sets with 12 agents and 8 tasks, our algorithm managed to find optimal solutions after only evaluating approximately 0.000003% of the possible solutions. In the hardest of the problem sets with 12 agents and 8 tasks, our algorithm managed to find a 80% efficient solution after only evaluating approximately 0.000006% of the possible solutions. I denna uppsats presenteras en ny algoritm som är designad för att förbättra samarbetsförmågan hos agenter som verkar i realtidssystem. Vi studerar även koalitionsbildnings- och uppgiftstilldelningsproblemen inom realtidsstrategispel, och löser dessa problem optimalt genom att utveckla en effektiv anytime-algoritm som löser det kombinerade koalitionsbildnings- och uppgiftstilldelningsproblemet, inom vilket disjunkta koalitioner formas och tilldelas uppgifter. Detta problem, som vi kallar samarbetsproblemet, är en typ av optimeringsproblem som har många viktiga motsvarigheter i verkligheten, exempelvis för skapandet av arbetsgrupper som skall lösa specifika problem, eller för att ta fram optimala tvärfunktionella team med tilldelade uppgifter. Den presenterade algoritmens prestanda utvärderas dels genom att använda simulerade problem av olika svårighetsgrad, men också genom att använda verkliga problembeskrivningar från det kommersiella strategispelet Europa Universalis 4, vilket är ett spel som agenter måste samarbeta i för att effektivt uppnå deras mål. Att koordinera agenter i sådana spel är svårt, men vår algoritm åstadkommer detta genom att systematiskt söka efter de optimala agentgrupperingarna för ett antal givna uppgifter. Resultaten från de simulerade problemen visar att vår algoritm effektivt löser samarbetsproblemet genom att systematiskt sålla bort suboptimala delar av sökrymden. I dessa tester lyckas vår algoritm generera högkvalitativa anytime-lösningar. Till exempel, i de enklaste problemen med 12 agenter och 8 uppgifter lyckas vår algoritm hitta den optimala lösningen efter det att den endast utvärderat 0.000003% av de möjliga lösningarna. I de svåraste problemen med 12 agenter och 8 uppgifter lyckas vår algoritm hitta en lösning som är 80% från den optimala lösningen efter det att den endast utvärderat 0.000006% av de möjliga samarbetsstrukturerna.

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Meier, Tobias, Michael Ernst, Andreas Frey, and Wolfram Hardt. "Enhancing Task Assignment in Many-Core Systems by a Situation Aware Scheduler." Universitätsbibliothek Chemnitz, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-227009.

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The resource demand on embedded devices is constantly growing. This is caused by the sheer explosion of software based functions in embedded systems, that are growing far faster than the resources of the single-core and multi-core embedded processors. As one of the limitation is the computing power of the processors we need to explore ways to use this resource more efficiently. We identified that during the run-time of the embedded devices the resource demand of the software functions is permanently changing dependent on the device situation. To enable an embedded device to take advantage of this dynamic resource demand, the allocation of the software functions to the processor must be handled by a scheduler that is able to evaluate the resource demand of the software functions in relation to the device situation. This marks a change in embedded devices from static defined software systems to dynamic software systems. Above that we can increase the efficiency even further by extending the approach from a single device to a distributed or networked system (many-core system). However, existing approaches to deal with dynamic resource allocation are focused on individual devices and leave the optimization potential of manycore systems untouched. Our concept will extend the existing Hierarchical Asynchronous Multi-Core Scheduler (HAMS) concept for individual devices to many-core systems. This extension introduces a dynamic situation aware scheduler for many-core systems which take the current workload of all devices and the system-situation into account. With our approach, the resource efficiency of an embedded many-core system can be increased. The following paper will explain the architecture and the expected results of our concept.

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Pedroncelli, Giovanni. "Distributed Discrete Consensus Algorithms: Theory and Applications for the Task Assignment Problem." Doctoral thesis, Università degli studi di Trieste, 2015. http://hdl.handle.net/10077/10975.

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2013/2014 Distributed computation paradigms belong to a research field of increasing interest. Using these algorithms will allow to exploit the capabilities of large scale networks and systems in the near future. Relevant information for the resolution of a problem are distributed among a network of agents with limited memory and computation capability; the problem is solved only by means of local computation and message exchange between neighbour agents. In this thesis we consider the multi-agent assignment problem dealt with distributed computation: a network of agents has to cooperatively negotiate the assignment of a number of tasks by applying a distributed discrete consensus algorithm which defines how the agents exchange information. Consensus algorithms are dealt with always more frequently in the related scientific literature. Therefore, in the first chapter of this thesis we present a related literature review containing some of the most interesting works concerning distributed computation and, in particular, distributed consensus algorithms: some of these works deal with the theory of consensus algorithms, in particular convergence properties, others deal with applications of these algorithms. In the second chapter the main contribution of this thesis is presented: aniterative distributed discrete consensus algorithm based on the resolution of local linear integer optimization problems (L-ILPs) to be used for the multi-agent assignment problem. The algorithm is characterized by theorems proving convergence to a final solution and the value of the convergence time expressed in terms of number of iterations. The chapter is concluded by a performance analysis by means of the results of simulations performed with Matlab software. All the results are presented considering two different network topologies in order to model two different real life scenarios for the connection among agents. The third chapter presents an interesting application of the proposed algorithm: a network of charging stations (considered as agents) has to reach a consensus on the assignment of a number of Electric Vehicles (EVs) requiring to be recharged. In this application the algorithm proposed in the previous chapter undergoes several modifications in order to model effectively this case: considering the inter-arrival times of vehicles to a charging station, a non-linear element appears in the objective function and therefore a novel algorithm to be performed before the assignment algorithm is presented; this algorithm defines the order in which the assigned vehicles have to reach a charging station. Moreover, a communication protocol is proposed by which charging stations and vehicles can communicate and exchange information also allowing charging stations to send to each assigned vehicle the maximum waiting time which can pass before a vehicle loses its right to be recharged. The chapter ends with an example of application of the rivisited assignment algorithm. In the fourth and last chapter, we present an application in an industrial environment: a network of Autonomous Guided Vehicles (AGVs) in a warehouse modeled as a graph has to perform the distributed discrete consensus algorithm in order to assign themselves a set of destinations in which some tasks are located. This application deals not only with the task assignment problem but also with the following destination reaching problem: therefore a distributed coordination algorithm is proposed which allows the AGVs to move into the warehouse avoiding collisions and deadlock. An example of the control strategy application involving both the assignment and coordination algorithms concludes this chapter. XXVII Ciclo 1986

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Staffolani, Alessandro. "A Reinforcement Learning Agent for Distributed Task Allocation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20051/.

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Al giorno d'oggi il reinforcement learning ha dimostrato di essere davvero molto efficace nel machine learning in svariati campi, come ad esempio i giochi, il riconoscimento vocale e molti altri. Perciò, abbiamo deciso di applicare il reinforcement learning ai problemi di allocazione, in quanto sono un campo di ricerca non ancora studiato con questa tecnica e perchè questi problemi racchiudono nella loro formulazione un vasto insieme di sotto-problemi con simili caratteristiche, per cui una soluzione per uno di essi si estende ad ognuno di questi sotto-problemi. In questo progetto abbiamo realizzato un applicativo chiamato Service Broker, il quale, attraverso il reinforcement learning, apprende come distribuire l'esecuzione di tasks su dei lavoratori asincroni e distribuiti. L'analogia è quella di un cloud data center, il quale possiede delle risorse interne - possibilmente distribuite nella server farm -, riceve dei tasks dai suoi clienti e li esegue su queste risorse. L'obiettivo dell'applicativo, e quindi del data center, è quello di allocare questi tasks in maniera da minimizzare il costo di esecuzione. Inoltre, al fine di testare gli agenti del reinforcement learning sviluppati è stato creato un environment, un simulatore, che permettesse di concentrarsi nello sviluppo dei componenti necessari agli agenti, invece che doversi anche occupare di eventuali aspetti implementativi necessari in un vero data center, come ad esempio la comunicazione con i vari nodi e i tempi di latenza di quest'ultima. I risultati ottenuti hanno dunque confermato la teoria studiata, riuscendo a ottenere prestazioni migliori di alcuni dei metodi classici per il task allocation.

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Mazur, Lukasz Maciej. "Project Task: Member Assignment Using Design Structure Matrix and Genetic Algorithm in Concurrent Engineering Project Management." Thesis, Montana State University, 2005. http://etd.lib.montana.edu/etd/2005/mazur/MazurL0505.pdf.

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In concurrent engineering, project tasks generally require the establishment of multifunctional teams to simultaneously consider various activities throughout the entire product life cycle. Team members from different functional departments of the company interact in every phase of development activities to design products and processes concurrently. This concurrent strategy increases the complexity of product development and design processes and makes teams difficult to organize. Without effective task coordination and team organization, the lack of communication and cooperation among team members in a large group of tasks could seriously delay the project completion. This research provides an integrated solution to overcome these difficulties. This research aims to model both project tasks and team members for the task-member assignments. To accomplish this, we develop an integrated framework that includes three major components: a project task model, a team member model and a task-member assignment model. The project task model optimizes the complex task structure using a Genetic Algorithm (GA), while Design Structure Matrix (DSM) identifies the three major project task types: independent, dependent, and interdependent. The team member model provides a quantitative representation for three important team member characteristics, namely functional knowledge, teamwork capability and working relationship. Analytic Hierarchy Process (AHP) and personality profiling using Myers-Briggs Type Indicator (MBTI) are used to obtain ratings of team member characteristics. According to the project task structure, quantified team member characteristics, and each member\'s workload schedule, the task-member assignment model accomplishes the ultimate goal of this research - assigning the right team members to the right tasks at the right time. The effectiveness of the developed methodology is demonstrated by an illustrative example.

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Viguria, Jimenez Luis Antidio. "Distributed Task Allocation Methodologies for Solving the Initial Formation Problem." Thesis, Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24731.

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Mobile sensor networks have been shown to be a powerful tool for enabling a number of activities that require recording of environmental parameters at various spatial and temporal distributions. These mobile sensor networks could be implemented using a team of robots, usually called robotic sensor networks. This type of sensor network involves the coordinated control of multiple robots to achieve specific measurements separated by varied distances. In most formation measurement applications, initialization involves identifying a number of interesting sites to which mobility platforms, instrumented with a variety of sensors, are tasked. This process of determining which instrumented robot should be tasked to which location can be viewed as solving the task allocation problem. Unfortunately, a centralized approach does not fit in this type of application due to the fault tolerance requirements. Moreover, as the size of the network grows, limitations in bandwidth severely limits the possibility of conveying and using global information. As such, the utilization of decentralized techniques for forming new sensor topologies and configurations is a highly desired quality of robotic sensor networks. In this thesis, several distributed task allocation algorithms will be explained and compared in different scenarios. They are based on a market approach since our interest is not only to obtain a feasible solution, but also an efficient one. Also, an analysis of the efficiency of those algorithms using probabilistic techniques will be explained. Finally, the task allocation algorithms will be implemented on a real system consisted of a team of six robots and integrated in a complete robotic system that considers obstacle avoidance and path planning. The results will be validated in both simulations and real experiments.

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Karasakal, Orhan. "Optimal Air Defense Strategies For A Naval Task Group." Phd thesis, Ankara : METU, 2004. http://etd.lib.metu.edu.tr/upload/12604699/index.pdf.

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Thesis (Ph. D.)--Middle East Technical University, 2004. Keywords: Air Defense, Naval Task Group, Formation, Weapon Target Allocation Problem, Military Operations Research, Quadratic Assignment, Location.

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Ramesh, Vasanth Kumar. "A game theoretic framework for dynamic task scheduling in distributed heterogeneous computing systems." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001115.

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Yilmaz, Yagiz Sevil. "Dspptool: A Tool To Support Distributed Software Project Planning." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12604761/index.pdf.

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This thesis focuses on the development of a distributed software project planning tool that enables more than one participant to prepare the different parts of the project scope, schedule and task assignment by allowing to utilize the predefined organizational level processes. For this purpose, we discuss the need for a distributed software project planning tool, identify tool requirements and compare available tools with respect to the requirements. In addition, we evaluate the tool based on two criteria: first one is the tool&rsquo s adequacy to meet the identified functional attributes and the second one is the validation of the tool by utilizing the data of the project schedule of a real project. This tool enables preparation of project scope, schedule and task assignments in a more effective, accurate and seamless way.

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Pippin, Charles Everett. "Trust and reputation for formation and evolution of multi-robot teams." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50376.

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Agents in most types of societies use information about potential partners to determine whether to form mutually beneficial partnerships. We can say that when this information is used to decide to form a partnership that one agent trusts another, and when agents work together for mutual benefit in a partnership, we refer to this as a form of cooperation. Current multi-robot teams typically have the team's goals either explicitly or implicitly encoded into each robot's utility function and are expected to cooperate and perform as designed. However, there are many situations in which robots may not be interested in full cooperation, or may not be capable of performing as expected. In addition, the control strategy for robots may be fixed with no mechanism for modifying the team structure if teammate performance deteriorates. This dissertation investigates the application of trust to multi-robot teams. This research also addresses the problem of how cooperation can be enabled through the use of incentive mechanisms. We posit a framework wherein robot teams may be formed dynamically, using models of trust. These models are used to improve performance on the team, through evolution of the team dynamics. In this context, robots learn online which of their peers are capable and trustworthy to dynamically adjust their teaming strategy. We apply this framework to multi-robot task allocation and patrolling domains and show that performance is improved when this approach is used on teams that may have poorly performing or untrustworthy members. The contributions of this dissertation include algorithms for applying performance characteristics of individual robots to task allocation, methods for monitoring performance of robot team members, and a framework for modeling trust of robot team members. This work also includes experimental results gathered using simulations and on a team of indoor mobile robots to show that the use of a trust model can improve performance on multi-robot teams in the patrolling task.

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Martin, Megan Wydick. "Computational Studies in Multi-Criteria Scheduling and Optimization." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78699.

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Multi-criteria scheduling provides the opportunity to create mathematical optimization models that are applicable to a diverse set of problem domains in the business world. This research addresses two different employee scheduling applications using multi-criteria objectives that present decision makers with trade-offs between global optimality and the level of disruption to current operating resources. Additionally, it investigates a scheduling problem from the product testing domain and proposes a heuristic solution technique for the problem that is shown to produce very high-quality solutions in short amounts of time. Chapter 2 addresses a grant administration workload-to-staff assignment problem that occurs in the Office of Research and Sponsored Programs at land-grant universities. We identify the optimal workload assignment plan which differs considerably due to multiple reassignments from the current state. To achieve the optimal workload reassignment plan we demonstrate a technique to identify the n best reassignments from the current state that provides the greatest progress toward the utopian solution. Solving this problem over several values of n and plotting the results allows the decision maker to visualize the reassignments and the progress achieved toward the utopian balanced workload solution. Chapter 3 identifies a weekly schedule that seeks the most cost-effective set of coach-to-program assignments in a gymnastics facility. We identify the optimal assignment plan using an integer linear programming model. The optimal assignment plan differs greatly from the status quo; therefore, we utilize a similar approach from Chapter 2 and use a multiple objective optimization technique to identify the n best staff reassignments. Again, the decision maker can visualize the trade-off between the number of reassignments and the resulting progress toward the utopian staffing cost solution and make an informed decision about the best number of reassignments. Chapter 4 focuses on product test scheduling in the presence of in-process and at-completion inspection constraints. Such testing arises in the context of the manufacture of products that must perform reliably in extreme environmental conditions. Each product receives a certification at the successful completion of a predetermined series of tests. Operational efficiency is enhanced by determining the optimal order and start times of tests so as to minimize the make span while ensuring that technicians are available when needed to complete in-process and at-completion inspections We first formulate a mixed-integer programming model (MILP) to identify the optimal solution to this problem using IBM ILOG CPLEX Interactive Optimizer 12.7. We also present a genetic algorithm (GA) solution that is implemented and solved in Microsoft Excel. Computational results are presented demonstrating the relative merits of the MILP and GA solution approaches across a number of scenarios. Ph. D.

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Wang, Yating. "Trust-Based Service Management for Service-Oriented Mobile Ad Hoc Networks and Its Application to Service Composition and Task Assignment with Multi-Objective Optimization Goals." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/70965.

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With the proliferation of fairly powerful mobile devices and ubiquitous wireless technology, traditional mobile ad hoc networks (MANETs) now migrate into a new era of service-oriented MANETs wherein a node can provide and receive service from other nodes it encounters and interacts with. This dissertation research concerns trust management and its applications for service-oriented MANETs to answer the challenges of MANET environments, including no centralized authority, dynamically changing topology, limited bandwidth and battery power, limited observations, unreliable communication, and the presence of malicious nodes who act to break the system functionality as well as selfish nodes who act to maximize their own gain. We propose a context-aware trust management model called CATrust for service-oriented ad hoc networks. The novelty of our design lies in the use of logit regression to dynamically estimate trustworthiness of a service provider based on its service behavior patterns in a context environment, treating channel conditions, node status, service payoff, and social disposition as 'context' information. We develop a recommendation filtering mechanism to effectively screen out false recommendations even in extremely hostile environments in which the majority recommenders are malicious. We demonstrate desirable convergence, accuracy, and resiliency properties of CATrust. We also demonstrate that CATrust outperforms contemporary peer-to-peer and Internet of Things trust models in terms of service trust prediction accuracy against collusion recommendation attacks. We validate the design of trust-based service management based on CATrust with a node-to-service composition and binding MANET application and a node-to-task assignment MANET application with multi-objective optimization (MOO) requirements. For either application, we propose a trust-based algorithm to effectively filter out malicious nodes exhibiting various attack behaviors by penalizing them with trust loss, which ultimately leads to high user satisfaction. Our trust-based algorithm is efficient with polynomial runtime complexity while achieving a close-to-optimal solution. We demonstrate that our trust-based algorithm built on CATrust outperforms a non-trust-based counterpart using blacklisting techniques and trust-based counterparts built on contemporary peer-to-peer trust protocols. We also develop a dynamic table-lookup method to apply the best trust model parameter settings upon detection of rapid MANET environment changes to maximize MOO performance. Ph. D.

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Liao, Yan. "Decentralized Decision Making and Information Sharing in a Team of Autonomous Mobile Agents." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353101024.

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Jin, Huan. "Workforce planning in manufacturing and healthcare systems." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/5784.

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This dissertation explores workforce planning in manufacturing and healthcare systems. In manufacturing systems, the existing workforce planning models often lack fidelity with respect to the mechanism of learning. Learning refers to that employees’ productivity increases as they gain more experience. Workforce scheduling in the short term has a longer term impact on organizations’ capacity. The mathematical representations of learning are usually nonlinear. This nonlinearity complicates the planning models and provides opportunities to develop solution methodologies for realistically-sized instances. This research formulates the workforce planning problem as a mixed-integer nonlinear program (MINLP) and overcomes the limitations of cur- rent solution methods. Specifically, this research develops a reformulation technique that converts the MINLP to a mixed integer linear program (MILP) and proposes several techniques to speed up the solution time of solving the MILP. In organizations that use group work, workers learn not only by individual learning but also from knowledge transferred from team members. Managers face the decision of how to pair or team workers such that organizations benefit from this transfer of learning. Using a mathematical representation that incorporates both in- dividual learning and knowledge transfer between workers, this research considers the problem of grouping workers to teams and assigning teams to sets of jobs based on workers’ learning and knowledge transfer characteristics. This study builds a Mixed- integer nonlinear programs (MINP) for parallel systems with the objective of maximizing the system throughput and propose exact and heuristic solution approaches for solving the MINLP. In healthcare systems, we focus on managing medical technicians in medical laboratories, in particular, the phlebotomists. Phlebotomists draw specimens from patients based on doctors’ orders, which arrive randomly in a day. According to the literature, optimizing scheduling and routing in hospital laboratories has not been regarded as a necessity for laboratory management. This study is motivated by a real case at University of Iowa Hospital and Clinics, where there is a team of phlebotomists that cannot fulfill doctors requests in the morning shift. The goal of this research is routing these phlebotomists to patient units such that as many orders as possible are fulfilled during the shift. The problem is a team orienteering problem with stochastic rewards and service times. This research develops an a priori approach which applies a variable neighborhood search heuristic algorithm that improves the daily performance compared to the hospital practice.

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Wang, Chenxing. "DYNAMIC VOLTAGE SCALING FOR PRIORITY-DRIVEN SCHEDULED DISTRIBUTED REAL-TIME SYSTEMS." UKnowledge, 2007. http://uknowledge.uky.edu/gradschool_diss/571.

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Energy consumption is increasingly affecting battery life and cooling for real- time systems. Dynamic Voltage and frequency Scaling (DVS) has been shown to substantially reduce the energy consumption of uniprocessor real-time systems. It is worthwhile to extend the efficient DVS scheduling algorithms to distributed system with dependent tasks. The dissertation describes how to extend several effective uniprocessor DVS schedul- ing algorithms to distributed system with dependent task set. Task assignment and deadline assignment heuristics are proposed and compared with existing heuristics concerning energy-conserving performance. An admission test and a deadline com- putation algorithm are presented in the dissertation for dynamic task set to accept the arriving task in a DVS scheduled real-time system. Simulations show that an effective distributed DVS scheduling is capable of saving as much as 89% of energy that would be consumed without using DVS scheduling. It is also shown that task assignment and deadline assignment affect the energy- conserving performance of DVS scheduling algorithms. For some aggressive DVS scheduling algorithms, however, the effect of task assignment is negligible. The ad- mission test accept over 80% of tasks that can be accepted by a non-DVS scheduler to a DVS scheduled real-time system.

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Pilourdault, Julien. "Scalable algorithms for monitoring activity traces." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM040/document.

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Dans cette thèse, nous étudions des algorithmes pour le monitoring des traces d’activité à grande échelle. Le monitoring est une aptitude clé dans plusieurs domaines, permettant d’extraire de la valeur des données ou d’améliorer les performances d’un système. Nous explorons d’abord le monitoring de données temporelles. Nous présentons un nouveau type de jointure sur des intervalles, qui inclut des fonctions de score caractérisant le degré de satisfaction de prédicats temporels. Nous étudions ces jointures dans le contexte du batch processing (traitement par lots). Nous formalisons la Ranked Temporal Join (RTJ), une jointure qui combine des collections d’intervalles et retourne les k meilleurs résultats. Nous montrons comment exploiter les propriétés des prédicats temporels et de la sémantique de score associée afin de concevoir TKIJ , une méthode d’évaluation de requête distribuée basée sur Map-Reduce. Nos expériences sur des données synthétiques et réelles montrent que TKIJ est plus performant que les techniques de l’état de l’art et démontre de bonnes performances sur des requêtes RTJ n-aires sur des données temporelles. Nous proposons également une étude préliminaire afin d’étendre nos travaux sur TKIJ au domaine du stream processing (traitement de flots). Nous explorons ensuite le monitoring dans le crowdsourcing (production participative). Nous soutenons la nécessité d’intégrer la motivation des travailleurs dans le processus d’affectation des tâches. Nous proposons d’étudier une approche adaptative, qui évalue la motivation des travailleurs lors de l’exécution des tâches et l’exploite afin d’améliorer l’affectation de tâches qui est réalisée de manière itérative. Nous explorons une première variante nommée Individual Task Assignment (Ita), dans laquelle les tâches sont affectées individuellement, un travailleur à la fois. Nous modélisons Ita et montrons que ce problème est NP-Difficile. Nous proposons trois méthodes d’affectation de tâches qui poursuivent différents objectifs. Nos expériences en ligne étudient l’impact de chaque méthode sur la performance globale dans l’exécution de tâches. Nous observons que différentes stratégies sont dominantes sur les différentes dimensions de performance. En particulier, la méthode affectant des tâches aléatoires et correspondant aux intérêts d’un travailleur donne le meilleur flux d’exécution de tâches. La méthode affectant des tâches correspondant au compromis d’un travailleur entre diversité et niveau de rémunération des tâches donne le meilleur niveau de qualité. Nos expériences confirment l’utilité d’une affectation de tâches adaptative et tenant compte de la motivation. Nous étudions une deuxième variante nommée Holistic Task Assignment (Hta), où les tâches sont affectées à tous les travailleurs disponibles, de manière holistique. Nous modélisons Hta et montrons que ce problème est NP-Difficile et MaxSNP-Difficile. Nous développons des algorithmes d’approximation pour Hta. Nous menons des expériences sur des données synthétiques pour évaluer l’efficacité de nos algorithmes. Nous conduisons également des expériences en ligne et comparons notre approche avec d’autres stratégies non adaptatives. Nous observons que notre approche présente le meilleur compromis sur les différentes dimensions de performance In this thesis, we study scalable algorithms for monitoring activity traces. In several domains, monitoring is a key ability to extract value from data and improve a system. This thesis aims to design algorithms for monitoring two kinds of activity traces. First, we investigate temporal data monitoring. We introduce a new kind of interval join, that features scoring functions reflecting the degree of satisfaction of temporal predicates. We study these joins in the context of batch processing: we formalize Ranked Temporal Join (RTJ), that combine collections of intervals and return the k best results. We show how to exploit the nature of temporal predicates and the properties of their associated scored semantics to design TKIJ , an efficient query evaluation approach on a distributed Map-Reduce architecture. Our extensive experiments on synthetic and real datasets show that TKIJ outperforms state-of-the-art competitors and provides very good performance for n-ary RTJ queries on temporal data. We also propose a preliminary study to extend our work on TKIJ to stream processing. Second, we investigate monitoring in crowdsourcing. We advocate the need to incorporate motivation in task assignment. We propose to study an adaptive approach, that captures workers’ motivation during task completion and use it to revise task assignment accordingly across iterations. We study two variants of motivation-aware task assignment: Individual Task Assignment (Ita) and Holistic Task Assignment (Hta). First, we investigate Ita, where we assign tasks to workers individually, one worker at a time. We model Ita and show it is NP-Hard. We design three task assignment strategies that exploit various objectives. Our live experiments study the impact of each strategy on overall performance. We find that different strategies prevail for different performance dimensions. In particular, the strategy that assigns random and relevant tasks offers the best task throughput and the strategy that assigns tasks that best match a worker’s compromise between task diversity and task payment has the best outcome quality. Our experiments confirm the need for adaptive motivation-aware task assignment. Then, we study Hta, where we assign tasks to all available workers, holistically. We model Hta and show it is both NP-Hard and MaxSNP-Hard. We develop efficient approximation algorithms with provable guarantees. We conduct offline experiments to verify the efficiency of our algorithms. We also conduct online experiments with real workers and compare our approach with various non-adaptive assignment strategies. We find that our approach offers the best compromise between performance dimensions thereby assessing the need for adaptability

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Howell, Tina L. "Identity and international assignments : a discourse analysis of expatriatesâ€™ talk about short-term and long-term assignment experiences." Thesis, University of Nottingham, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431261.

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Silva, Fabiana Simões e. "Designação de tarefas em aplicações de multiprocessadores de processamento digital de sinal utilizando algoritmos genéticos." Universidade Federal de São Carlos, 2003. https://repositorio.ufscar.br/handle/ufscar/3748.

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Made available in DSpace on 2016-06-02T19:52:03Z (GMT). No. of bitstreams: 1 DissFSS.pdf: 471874 bytes, checksum: 52bc0458fea36b975556da8d0d9d80ca (MD5) Previous issue date: 2003-08-14 This work consists in the development of genetic algorithms for the Task-to-Processo Assignment Problem in multiprocessor applications. Specifically, the objective is to find the task-to-processor assignment that minimizes the total delay in a particular multiprocessor digital signal processing architecture. We present a description of our algorithm implementations and the results found with a set of 117 randomly generated and real-life instances. The algorithms performance is compared with the results provided by a competitive dynamic list heuristic and a multiple start search algorithm. The results indicate lower delays in more than 68% of the instances, at a higher computational cost. O objetivo deste projeto consiste no desenvolvimento de algoritmos genéticos para resolução do problema de designação de tarefas em multiprocessadores de processamento digital de sinal (PDS). Especificamente, busca-se minimizar o atraso total em uma arquitetura de multiprocessadores particular, bastante utilizada em sistemas reais. Neste trabalho são apresentadas implementações de algoritmos genéticos, e os resultados computacionais decorrentes de sua aplicação a um conjunto de 117 exemplos gerados aleatoriamente e extraídos de contextos reais. O desempenho dos algoritmos é analisado, comparando-se a qualidade das soluções e os tempos computacionais requeridos com os obtidos por uma heurística competitiva da literatura e por um algoritmo de busca multiple starts. Os algoritmos genéticos obtiveram menores valores de atraso em mais de 68% dos exemplos, a um tempo computacional maior.

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Kudirkaitė, Birutė. "Smulkaus ir vidutinio dydžio įmonių vidaus valdymo sistema." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2006. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20060111_131811-14622.

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The main problem analyzed in this work was the need to design and create an internal management system for small and medium enterprices, that would meet their particular requests on usability, extensibility and adaptability at available costs. As solution to this problem was deep analysis of available systems of that kind, and creation of our own system, that had met necessary requirements and even more – had some technological improvements, such as reduced number of users actions, necessary to make things done. This was implemented with the help of AJAX programming technique and a special framework, created for this system. This system can be used to manage projects in a small or medium enterprice, collect statistics about projects, staff work, distribute project tasks to staff and monitor the implementation. There also is an oportunity to generate various reports, such as summary of accomplished tasks for every employee, lists of project tasks and their status and so on. This system can be easily installed in any enterprice because of it‘s client – server architecture, where client connects to the server with the help of a web browser, that can be found in every computer, so there is no need to install system in all computers in enterprice. In that way maintenance costs are reduced, because all you have to look after is server with a system on it.

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Ivan, Mezei. "Aukcijski agregacioni algoritmi za izbor izvršioca u bežičnim muti-hop mrežama elektronskih senzora i aktuatora." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2012. http://dx.doi.org/10.2298/NS20120712MEZEI.

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Polazeći od prostog aukcijskog algoritma (SAP) za izbor izvršioca ubežičnim senzorskim i aktuatorskim mrežama u ovoj disertaciji suuvedena dva poboljšanja tog algoritma (multi-hop komunikacija ilokalizacija). Predloženo je i pet novih aukcijskih agregacionihalgoritama koji koriste tehniku agregacije da bi smanjilikomunikacione troškove. Treća grupa doprinosa ove disertacije seodnosi na primenu aukcija za poboljšanje postojećeg iMesh algoritmaza pronalaženje izvršioca. Eksperimentalni rezultati pokazujusvojstva algoritama i njihove prednosti u odnosu na postojeće. Two improvements of the simple auction protocol (SAP) for the taskassignment in wireless sensor and actuator networks is proposed in thisdissertation (multi-hop communications and localization). Five new auctionaggregation algorithms for the task assignment are proposed with the goal tominimize the communication costs. One of the auction aggregation algorithmsis also used to improve existing iMesh service discovery algorithm.Experimental results show the characteristics of the proposed algorithms andadvantages over the existing ones.

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Pagliarani, Ridolfi Sara. "I sistemi di Order Picking e le decisioni critiche per la loro progettazione e ottimizzazione: il caso Tulips S.r.l." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Il magazzino è la parte fondamentale del business di molte realtà aziendali, tra cui si trovano i sistemi di order-picking (OPS) ovvero quei sistemi in cui i prodotti vengono stoccati a magazzino per essere poi prelevati meccanicamente o manualmente dagli operatori per soddisfare la domanda dei clienti interni e/o esterni all’azienda. Con questo elaborato si vuole fornire un quadro teorico sui sistemi OP, sulle loro caratteristiche e tipologie e sulle aree decisionali al momento della loro progettazione e/o ottimizzazione. Successivamente, servendosi dell’azienda Tulips Srl, si vuole fornire un’analisi del sistema a prelievo frazionato in atto nella loro sede di Cesena e il procedimento utilizzato per la progettazione parziale del magazzino nella nuova sede di Rimini. Lo scopo principale della progettazione di un OPS è quello di diminuire il tempo e la distanza di attraversamento dell’operatore durante il picking. Questo perché come dimostrato da Tompkins il tempo speso da un operatore per gli spostamenti utili al prelievo ammonta al 50% sul totale delle operazioni, andando conseguentemente ad incidere sui costi operativi del magazzino. Per questo motivo, lo scopo della progettazione, che si è basato sulle scelte di routing and sequencing problem e sui problemi di product allocation and assignment, ha avuto come scopo principale quello di condensare l’area del picking. La strategia utilizzata per l’assegnamento è stata la class-based storage policy e il criterio di assegnamento dei prodotti alle classi utilizzato è stato il Popularity assignment rule. Una volta assegnati i prodotti alle classi è stati possibile assegnare le classi alle corsie in modo da avere i prodotti desiderabili all’inizio del percorso. Questa pratica ha permesso di convogliare il maggior numero di prelievi in una certa area, quella più vicina al punto di stoccaggio degli ordini pronti nei reparti, e di ridurre conseguentemente le distanze di viaggio.

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Nabais, Raquel Nogueira. "Calendarização das visitas dos artistas da operação nariz vermelho aos hospitais." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/18452.

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Mestrado em Métodos Quantitativos para a Decisão Económica e Empresarial Este trabalho final de mestrado foi desenvolvido sob a forma de projeto, em parceria com a Operação Nariz Vermelho (ONV), estando o seu tema ligado à afetação dos artistas profissionais, Doutores Palhaços, da ONV às visitas a realizar aos vários hospitais, abrangidos pelo programa de visitas desta instituição, na região da Grande Lisboa. O objetivo deste projeto é facilitar a execução desta tarefa que, nos tempos que decorrem, é uma tarefa bastante morosa e trabalhosa para os colaboradores da ONV. Assim, e de forma a atingir todos os objetivos pensados pela instituição, foi de extrema importância, numa fase inicial, fazer o levantamento de todos as especificidades da calendarização das visitas. Depois, e recorrendo a estudos já realizados, foi formulado em Programação Linear Binária Mista um modelo de otimização capaz de gerar uma solução para dar resposta ao problema do planeamento das visitas. Adicionalmente, foi também desenvolvido um programa recorrendo ao uso da linguagem Visual Basic for Applications do Excel. Este é capaz de ler os dados do problema e colocá-los como parâmetros do software OpenSolver, executar o software de forma automática, escrever e estruturar a solução gerada com o intuito de possibilitar a análise e o uso desta pelos colaboradores da ONV. This Masters Final Work was developed in the form of a project in partnership with Operação Nariz Vermelho (ONV). This work is related to the assignment of ONV professional artists, Clown Doctors, to visits to be made to the various hospitals, covered by the visits program of this institution, in region of Lisbon. The objective of this project is to make easier the execution of this task, which, currently, is a very time consuming and laborious task for the staff of the institution. Thus, and in order to achieve all the objectives considered by the institution, it was extremely important, at an initial stage, to survey all the specific aspects of the visit schedule. Then, and basing our work on studies previously carried out, an optimization model was formulated using a Mixed Binary Linear Programming model capable of generating a solution to the problem for assignment of the visits. In addition, a program was developed using the Visual Basic for Applications language available in Excel. This software can read the data of the problem and place them as parameters of the OpenSolver software, execute the software automatically, write and structure the generated solution in order to allow the analysis and usage of this solution by ONV employees. info:eu-repo/semantics/publishedVersion

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Mtshali, Progress Q. T. "Minimizing Parallel Virtual Machine [PVM] Tasks Execution Times Through Optimal Host Assignments." NSUWorks, 2000. http://nsuworks.nova.edu/gscis_etd/740.

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PVM is a message passing system that is designed to run parallel programs across a network of workstations. Its default scheduler uses the round-robin scheduling algorithm to assign tasks to hosts on the virtual machine. This task assignment strategy is deficient when it comes to a network of heterogeneous workstations. Heterogeneity could be software related, hardware related or both. The default scheduler does not attempt to assign a PVM task to a node within the virtual machine that best services the PVM task. The problem with this allocation scheme is that resource requirements for each task that must be assigned to a computer vary. The default scheduler could assign a computation intensive parallel task to a computer that has a slow central processing unit (CPU) thus taking longer to execute the task to completion (Ju and Wang, 1996). It is also possible that a communication bound process is assigned to a computer with very low bandwidth. The scheduler needs to assign tasks to computers in such a way that tasks are assigned to computers that will fulfill the resource requirements of a given task: schedule and balance the work load in such a way that the overall execution time is minimized. In this dissertation, a stochastic task assignment method was developed that not only takes into account the heterogeneity of the hosts but also utilizes state information for the tasks to be assigned, and hosts onto which they are assigned. Task assignment methods that exist today, such as used in Condor, PvmJobs, CROP, and Matchmaker tend to be designed for a homogenous environment using the UNIX operating system or its variants. They also tend to use operating system data structures that are sometimes not common across most architectures. This makes it difficult to port them to other architectures. The task assignment method that was developed in this dissertation was initially implemented across a PVM environment consisting of a network of Win32® architecture, Linux architecture, and Sun Solaris architecture. A portable prototype implementation that runs across these architectures was developed. A simulator based on the CSIMIS toolkit was developed and utilized to study the behavior of the M/G/I queueing model on which the task assignment is based. Four PVM programs were run on a PVM environment that consisted of six nodes. The run-times of these progran1s were measured for both the default scheduler and the method proposed in this dissertation. The analysis of the run-times suggests that the proposed task assignment method works better for PVM programs that are CPU intensive than those that are small and do not require a lot of CPU time. The reason for this behavior can be attributed to high overheard that is required to compute the task assignment probabilities for all the hosts in the PVM environment. However, the proposed method appears to minimize execution times for the CPU intensive tasks. The ease with which external resource managers in the PVM environment can be incorporated into the PVM environment at run-time makes this proposed method an alternative to the default scheduler. More work needs to be done before the results that were obtained from this dissertation could be generalized, if at all possible, across the board. A number of recommendations on the reduction of overheard are specified at the end of this report.

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MORBEE, MARLEEN. "Optimized information processing in resource-constrained vision systems. From low-complexity coding to smart sensor networks." Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/12126.

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Vision systems have become ubiquitous. They are used for traffic monitoring, elderly care, video conferencing, virtual reality, surveillance, smart rooms, home automation, sport games analysis, industrial safety, medical care etc. In most vision systems, the data coming from the visual sensor(s) is processed before transmission in order to save communication bandwidth or achieve higher frame rates. The type of data processing needs to be chosen carefully depending on the targeted application, and taking into account the available memory, computational power, energy resources and bandwidth constraints. In this dissertation, we investigate how a vision system should be built under practical constraints. First, this system should be intelligent, such that the right data is extracted from the video source. Second, when processing video data this intelligent vision system should know its own practical limitations, and should try to achieve the best possible output result that lies within its capabilities. We study and improve a wide range of vision systems for a variety of applications, which go together with different types of constraints. First, we present a modulo-PCM-based coding algorithm for applications that demand very low complexity coding and need to preserve some of the advantageous properties of PCM coding (direct processing, random access, rate scalability). Our modulo-PCM coding scheme combines three well-known, simple, source coding strategies: PCM, binning, and interpolative coding. The encoder first analyzes the signal statistics in a very simple way. Then, based on these signal statistics, the encoder simply discards a number of bits of each image sample. The modulo-PCM decoder recovers the removed bits of each sample by using its received bits and side information which is generated by interpolating previous decoded signals. Our algorithm is especially appropriate for image coding. Morbee, M. (2011). Optimized information processing in resource-constrained vision systems. From low-complexity coding to smart sensor networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12126 Palancia

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Breuss, Fritz, and Markus Eller. "Efficiency and federalism in the European Union. The optimal assignment of policy tasks to different levels of government." Forschungsinstitut für Europafragen, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/1752/1/document.pdf.

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This paper surveys the theoretical and empirical research on the efficient assignment of policy tasks to different levels of government and applies the results on the delimitation of competences within the European Union. The main results are: (i) A precise derivation of an optimal degree of decentralisation is not possible because of mixed theoretical suggestions. The adequate degree of decentralisation has to be detected case-by-case. (ii) Systematic evidence on direct relationships between economic performance and fiscal decentralisation is ambiguous and scarce. (iii) Comparing the de facto delimitation of EU-competences with the normative recommendations, remarkable discrepancies arise in the fields of agriculture and defence. (iv) The establishment of a flexible assignment-scheme by the European Convention is an undeniable necessity in order to guarantee reversibility and to cope efficiently with changing general conditions. (author's abstract) Series: EI Working Papers / Europainstitut

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Pan, Simin, and Min Qiao. "The personal-related factors to expatriates’ task performance and contextual performance : Considering the influence of personality traits, competences/skills, self-willingness and previous experience." Thesis, Halmstad University, School of Business and Engineering (SET), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-5618.

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AbstractPurposeThe purpose of this paper is to find out which personal-related factors are influencing on the task performance and the contextual performance of expatriate managers.Design/Methodology/ApproachThis research adopts a qualitative approach, using semi-structured interviews with key-information from the managers taking international assignments.FindingsThe findings indicate that there is a series of significant associations between personal-related factors (personality traits, competences/skills, self-willingness and previous experience) and the performance of expatriates’ assignments.Research limitations/implicationsOne suggestion for further research is to explore deeper and more comprehensive on other less important factors or the important factors which we are overlooking; it also could be more comprehensive on the factors that relate to the performance of expatriates.Practical implicationsWe present a table of the relationship between personality traits, competences/skills, self-willingness, previous experience and the job performance of expatriates’ assignments. Thus, HR departments can follow this guidance when selecting expatriates to manage overseas assignments. Furthermore, individuals can take this model as a reference when making decisions for their career lives.KeywordsExpatriate assignments, personality traits, self-willingness, competences/skills, previous experience, job performance, task performance, contexture performancePaper typeMaster Thesis

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Lombaard, Malinda. "Task-based assessment for specific purpose Sesotho for personnel in the small business corporation." Thesis, Link to the online version, 2006. http://hdl.handle.net/10019/235.

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Hernandez, Pérez Adrian. "But, that’s how they talk in movies so it’s only fair! : Three Teachers’ Perspectives on the Emergence of Slang in Student Assignments in Swedish Upper-Secondary Schools." Thesis, Jönköping University, Högskolan för lärande och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-49466.

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Social media, movies and games all have something in common – they all have a tendency to foster slang. Adolescents of today consume these mediums virtually every day, and thus they easily adopt these slang terms faster than any other group. As they are influenced by the slang and informal style used in these mediums, this type of language use may become dominant and spill over on their academic English. The main objective of this study is to investigate the opinions of three Swedish upper-secondary EFL teachers through qualitative interviews. In the interviews, the teachers presented their opinions in relation to whether the usage of slang is evident in their students’ assignments as well as their opinions on the potential possibilities and challenges involved in working with slang in the classroom. The teachers noticed an emergence of informal language, and slang to a certain extent, in their students’ assignments. Profanities occurred as well, albeit more rarely and more so through speech. The teachers emphasized the importance of addressing slang by way of marking or discussion. They mostly saw the benefits of working with slang as a theme in the classroom due to it potentially being a fun learning experience and an opportunity to learn about cultural differences in the English-speaking world. However, the challenges were that it requires a certain amount interest from the teacher and it may take time that could be spent working on other goals.

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Pishdad-Bozorgi, Pardis. "Case-based Study and Analysis of Integrated Project Delivery (IPD) Approach and Trust-Building Attributes." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77143.

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The goal of this Ph.D. research is to explore the IPD contractual strategies, to highlight the elements that distinguish IPD from a traditional delivery approach, to analyze how trust-based relationships are established and promoted, and to demonstrate if/how trust and IPD contractual principles correlate. The result of this research will promote the understanding of the industry on the strategies that promote trust and integration through real world case studies. The significance of the subject becomes more evident when reflecting on the current industry's crisis: productivity loss, fragmented delivery process, and lack of trust and collaboration. Through a literature review a Project Delivery and Contracting Strategies (PDCS) framework, an IPD traits framework, and a trust-Building framework are developed. The frameworks are used as the organizational tools to structure and inquire relevant information on the two IPD projects. An expert panel is assembled to discuss the frameworks and the findings of literature analysis and to seek the industry's insight on the units of analysis for contract, and the units of measure for trust. The units of analysis for contract are elements, such as strategies for risks/rewards sharing, liability considerations, decision making authority, and governance. The units of measure for trust are the individuals' perception, and the trust-building attributes as outlined in table 4-1. Two IPD projects were selected and their contract agreements were studied. A questionnaire including both open-ended questions and multiple choice questions was developed based on the information collected through: 1. the IPD agreements in each case study, 2. the literature-based frameworks on trust and project delivery contracting strategies. Accordingly, two IPD case studies are developed following the analysis of their IPD agreements and the individual one-on-one interviews with their key IPD players. The trust-building framework presented in this work includes a series of techniques that the contracting parties can follow when establishing their contractual and managerial strategies and also when interacting with each other. Ph. D.

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Kába, Daniel. "Nové trendy v podnikání - Multidimenzionální rozhodování při outsourcingu účetních prací." Doctoral thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2009. http://www.nusl.cz/ntk/nusl-233718.

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The work deals with multidimensional decision-making in the outsourcing of accounting work; it is designed for accounting companies with more than 30 employees. These companies deal with hundreds of accounting tasks of various extent and nature which would be assigned to individual accountants. Theoretically speaking, the problem of assigning tasks to individual accountants can be resolved by well-known methods of operational research; however, certain qualities of accountants are very vague and are hard to quantify. These qualities include creativity, loyalty, credibility towards clients, etc. The qualities make the application of traditional algorithms (e.g. scheduling algorithms) very difficult. Moreover, certain accounting tasks are unique and can be assigned only by experienced managers, not algorithms. This work focuses on a fuzzy description of tasks and accountants and a two-level assignment process. In preliminary sorting, individual tasks to be assigned are classified as routine, which can be assigned through a fuzzy algorithm, or non-routine, which are assigned to individual employees through the manager . fuzzy expert system dialogue.

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Grilo, Carina Filipa Quintino Mota. "Um problema de afetação de pessoal a clientes de uma empresa de serviços domésticos." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/7709.

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Mestrado em Decisão Económica e Empresarial O presente trabalho final de mestrado é apresentado sob a forma de trabalho de projeto, em que se aplicam métodos e conhecimentos de Investigação Operacional e Computação, adquiridos ao longo do curso, à resolução de um problema real com que se deparou uma instituição de solidariedade social, a Pressley Ridge. Este problema que a instituição designa de "projeto Marias" consiste em promover o encontro entre os pedidos de clientes que procuram quem lhes faça determinados serviços domésticos e as funcionárias da instituição com formação para prestar os serviços procurados. Para conciliar os pedidos com as disponibilidades das funcionárias é preciso fazer a afetação de funcionárias a clientes, respeitando um conjunto de condições impostas por ambas as partes. Neste trabalho propõe-se um modelo de programação linear binária para descrever matematicamente o problema. Este modelo tem por base o problema de afetação clássico, no qual se incorporam restrições adicionais. Foram desenvolvidas duas heurísticas, uma construtiva e uma melhorativa para resolver o problema nos casos em que a dimensão não permite a resolução pelo Solver Premium Pro (Premium Solver Pro, 2006-2013), um suplemento do Excel. Quer as heurísticas, quer os procedimentos de tratamento dos ficheiros de dados e de apresentação de resultados foram programados em Visual Basic. Para comparar os diferentes métodos de resolução é realizada experiência computacional considerando um conjunto de instâncias de teste. São apresentadas conclusões acerca da qualidade das heurísticas e do desempenho do Solver Premium do Excel. This final master thesis is presented in the form of work project on the application of methods and acquired knowledge on Operations Research and Computer Science to the resolution of a real problem found by a private institution of social solidarity, the Pressley Ridge. This problem designated by the institution as "Projeto Marias" has the purpose of matching the requests of customers looking for people who do certain domestic services and trained employees of the institution who can provide the requested services. To match the clients' requests and employees' availability one has to assign employees to customers, respecting a set of conditions imposed by both parts. This paper proposes a binary linear programming model to mathematically describe the problem. The model is based on the classic assignment problem, where additional constraints are incorporated. Two heuristics were developed to provide feasible solutions for large instances that Premium Solver Pro (Premium Solver Pro, 2006-2013), an Excel add-in, could not solve. Either the heuristics or the procedures for input and output data files management were coded in Visual Basic. To compare the different methods computational experiment was carried out on a set of test instances. Conclusions are drawn about the quality of the heuristics and the performance of Excel Solver Premium.

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Weng, Wenn-Yu, and 翁問漁. "Task Assignment Decision Support for WFMS -- Organizational Memory Task Assignment System (OMTAS)." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/00469592048263509258.

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碩士 國立臺灣大學 資訊管理研究所 90 Workflow has become a critical enabler in many enterprise applications including portals and e-business but is still insufficient from the knowledge management perspective. Especially in the area of task assignment, most commercial workflow software only supports role-based task assignment mechanism, which obviously doesn’t meet the need of today’s enterprises. This paper proposes a criteria-based task assignment mechanism, making use of such technologies as ontology, organizational memory, and dynamic task assignment, to retain and reuse the experience and knowledge created during the process of task assignment. It is aimed at retaining invaluable decision contents and promoting the decision quality of task assignment. From the management perspective, the proposed framework covers three areas of considerations on task assignment including competency, organizational structure and human relationships. Our ultimate objectives are to enhance the functionality of task assignment and promote the KM role of workflow.

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