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Academic literature on the topic 'Task assignment'

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Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Task assignment"

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Zhang, Li. "Complementarity, Task Assignment, and Incentives." Journal of Management Accounting Research 15, no. 1 (January 1, 2003): 225–46. http://dx.doi.org/10.2308/jmar.2003.15.1.225.

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This paper studies the effect of complementarity on task assignment decisions and the design of optimal incentives for employees in charge of multiple tasks. A principal hires two identical agents. Each agent performs two tasks involving complementarities when the tasks vary in the informativeness of their performance measures. The principal tension governing how tasks are assigned lies between the heterogeneity loss due to aggregate performance measures under broad task assignments and the resulting positive information externalities. Although the aggregate performance measures under broad task assignments preclude tailoring the strength of incentives to the nature of the task, these measures are more informative about agent efforts when tasks are complementary. This enhanced informativeness improves contracting efficiency by mitigating the implicit costs of the heterogeneity loss. The analysis applies not only to the task assignment decisions, but also more broadly to other organizational structure decisions.
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Li, Xiang, Yan Zhao, Xiaofang Zhou, and Kai Zheng. "Consensus-Based Group Task Assignment with Social Impact in Spatial Crowdsourcing." Data Science and Engineering 5, no. 4 (September 15, 2020): 375–90. http://dx.doi.org/10.1007/s41019-020-00142-0.

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Abstract With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely.
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Zhao, Yan, Jinfu Xia, Guanfeng Liu, Han Su, Defu Lian, Shuo Shang, and Kai Zheng. "Preference-Aware Task Assignment in Spatial Crowdsourcing." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2629–36. http://dx.doi.org/10.1609/aaai.v33i01.33012629.

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With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers’ temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers’ temporal preferences modeling and preference-aware task assignment. We model worker preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover worker preferences for different categories of tasks in different time slots. Several preference-aware task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.
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Bethke, Brett, Mario Valenti, and Jonathan How. "UAV Task Assignment." IEEE Robotics & Automation Magazine 15, no. 1 (March 2008): 39–44. http://dx.doi.org/10.1109/m-ra.2007.914931.

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Li, Yunhui, Liang Chang, Long Li, Xuguang Bao, and Tianlong Gu. "TASC-MADM: Task Assignment in Spatial Crowdsourcing Based on Multiattribute Decision-Making." Security and Communication Networks 2021 (August 20, 2021): 1–14. http://dx.doi.org/10.1155/2021/5448397.

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The methodology, formulating a reasonable task assignment to find the most suitable workers for a task and achieving the desired objectives, is the most fundamental challenge in spatial crowdsourcing. Many task assignment approaches have been proposed to improve the quality of crowdsourcing results and the number of task assignment and to limit the budget and the travel cost. However, these approaches have two shortcomings: (1) these approaches are commonly based on the attributes influencing the result of task assignment. However, different tasks may have different preferences for individual attributes; (2) the performance and efficiency of these approaches are expected to be improved further. To address the above issues, we proposed a task assignment approach in spatial crowdsourcing based on multiattribute decision-making (TASC-MADM), with the dual objectives of improving the performance as well as the efficiency. Specifically, the proposed approach jointly considers the attributes on the quality of the worker and the distance between the worker and the task, as well as the influence differences caused by the task’s attribute preference. Furthermore, it can be extended flexibly to scenarios with more attributes. We tested the proposed approach in a real-world dataset and a synthetic dataset. The proposed TASC-MADM approach was compared with the RB-TPSC and the Budget-TASC algorithm using the real dataset and the synthetic dataset; the TASC-MADM approach yields better performance than the other two algorithms in the task assignment rate and the CPU cost.
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Kumar, Harendra, M. P. Singh, and Pradeep Kumar Yadav. "Optimal Tasks Assignment for Multiple Heterogeneous Processors with Dynamic Re-assignment." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (October 30, 2005): 528–35. http://dx.doi.org/10.24297/ijct.v4i2b2.3313.

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Distributed Computing System [DCS] has attracted several researchers by posing several challenging problems. In this paper we have developed a mathematical model for allocating “M” tasks of distributed program to “N” multiple processors (M>N) that minimizes the total cost of the program. Relocating the tasks from one processor to another at certain points during the course of execution of the program that contributes to the total cost of the running program has been taken into account. Most of the researchers have considered the cost for relocating the task from one processor to another processor at the end of the phase as a constant. But in real life situations the reallocating cost of the tasks may very processor to processor this is due to the execution efficiency of the processors. Phase-wise execution cost [EC], inter task communication cost [ITCT], residence cost [RC] of each task on different processors and relocation cost [REC] for each task have been considered while preparing a dynamic tasks allocation model.
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Baharian, Golshid, and Sheldon H. Jacobson. "Limiting Behavior of the Target-Dependent Stochastic Sequential Assignment Problem." Journal of Applied Probability 51, no. 04 (December 2014): 943–53. http://dx.doi.org/10.1017/s0021900200011906.

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The stochastic sequential assignment problem assigns distinct workers to sequentially arriving tasks with stochastic parameters. In this paper the assignments are performed so as to minimize the threshold probability, which is the probability of the long-run reward per task failing to achieve a target value (threshold). As the number of tasks approaches infinity, the problem is studied for independent and identically distributed (i.i.d.) tasks with a known distribution function and also for tasks that are derived from r distinct unobservable distributions (governed by a Markov chain). Stationary optimal policies are presented, which simultaneously minimize the threshold probability and achieve the optimal long-run expected reward per task.
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Baharian, Golshid, and Sheldon H. Jacobson. "Limiting Behavior of the Target-Dependent Stochastic Sequential Assignment Problem." Journal of Applied Probability 51, no. 4 (December 2014): 943–53. http://dx.doi.org/10.1239/jap/1421763320.

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The stochastic sequential assignment problem assigns distinct workers to sequentially arriving tasks with stochastic parameters. In this paper the assignments are performed so as to minimize the threshold probability, which is the probability of the long-run reward per task failing to achieve a target value (threshold). As the number of tasks approaches infinity, the problem is studied for independent and identically distributed (i.i.d.) tasks with a known distribution function and also for tasks that are derived from r distinct unobservable distributions (governed by a Markov chain). Stationary optimal policies are presented, which simultaneously minimize the threshold probability and achieve the optimal long-run expected reward per task.
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Zhu, Xiaojuan, Kuan-Ching Li, Jinwei Zhang, and Shunxiang Zhang. "Distributed Reliable and Efficient Transmission Task Assignment for WSNs." Sensors 19, no. 22 (November 18, 2019): 5028. http://dx.doi.org/10.3390/s19225028.

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Task assignment is a crucial problem in wireless sensor networks (WSNs) that may affect the completion quality of sensing tasks. From the perspective of global optimization, a transmission-oriented reliable and energy-efficient task allocation (TRETA) is proposed, which is based on a comprehensive multi-level view of the network and an evaluation model for transmission in WSNs. To deliver better fault tolerance, TRETA dynamically adjusts in event-driven mode. Aiming to solve the reliable and efficient distributed task allocation problem in WSNs, two distributed task assignments for WSNs based on TRETA are proposed. In the former, the sink assigns reliability to all cluster heads according to the reliability requirements, so the cluster head performs local task allocation according to the assigned phase target reliability constraints. Simulation results show the reduction of the communication cost and latency of task allocation compared to centralized task assignments. Like the latter, the global view is obtained by fetching local views from multiple sink nodes, as well as multiple sinks having a consistent comprehensive view for global optimization. The way to respond to local task allocation requirements without the need to communicate with remote nodes overcomes the disadvantages of centralized task allocation in large-scale sensor networks with significant communication overheads and considerable delay, and has better scalability.
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Li, Zhidu, Hailiang Liu, and Ruyan Wang. "Service Benefit Aware Multi-Task Assignment Strategy for Mobile Crowd Sensing." Sensors 19, no. 21 (October 27, 2019): 4666. http://dx.doi.org/10.3390/s19214666.

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Mobile crowd sensing (MCS) systems usually attract numerous participants with widely varying sensing costs and interest preferences to perform tasks, where accurate task assignment plays an indispensable role and also faces many challenges (e.g., how to simplify the complicated task assignment process and improve matching accuracy between tasks and participants, while guaranteeing submitted data credibility). To overcome these challenges, we propose a service benefit aware multi-task assignment (SBAMA) strategy in this paper. Firstly, service benefits of participants are modeled based on their task difficulty, task history, sensing capacity, and sensing positivity to meet differentiated requirements of various task types. Subsequently, users are then clustered by enhanced fuzzy clustering method. Finally, a gradient descent algorithm is designed to match task types to participants achieving the maximum service benefit. Simulation results verify that the proposed task assignment strategy not only effectively reduces matching complexity but also improves task completion rate.
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Dissertations / Theses on the topic "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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Lavoie, Marco Carleton University Dissertation Information and Systems Science. "Task assignment in a DSP multiprocessor environment." Ottawa, 1990.

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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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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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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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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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Books on the topic "Task assignment"

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Wassenhove, Luk N. van. A set partitioning heuristic for the generalized assignment problem. Fontainebleau: INSEAD, 1991.

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Dimarco, John D. Network-based heuristics for task assignment in large-scale distributed systems. Ottawa: National Library of Canada, 1995.

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Teays, Terry. Blazhko effect: Final report for contract NAS5-31840, task assignment 5788. [Washington, DC: National Aeronautics and Space Administration, 1996.

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DiMarco, John D. Network-based heuristics for task assignment in large-scale distributed systems. Toronto: University of Toronto, Dept. of Computer Science, 1995.

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Nicol, David. Static assignment of complex stochastic tasks using stochastic majorization. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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Caminer, Hilary. Just talk: Practical assignments for oral communication for GCSE English. London: Hodder and Stoughton, 1987.

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Nelson, Jennie. "This was an easy assignment": Examining how students interpret academic writing tasks. Berkeley, CA: Center for the Study of Writing, 1990.

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Gadzhiev, Nazirhan, Pavel Ivlichev, Natal'ya Ivlicheva, Ruslan Kornilovich, Elena Kolesnikova, Sergey Konovalenko, Mihail Lobanov, Nikolay Pilyugin, Aleksey Rebrov, and Natal'ya Trushina. Accounting. A collection of tasks, situations, and tests. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1037232.

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The implementation proposed in the collection of assignments and tests will allow cadets and students to master the basic techniques, equipment, accounting, learn to identify errors and irregularities in the registration work of the organization, to acquire skills of accounting. Comply with Federal state educational standard of higher education of the latest generation specialty 38.05.01 "Economic security". Designed for the students studying in higher educational institutions, including the Ministry of internal Affairs of Russia.
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Gdanskiy, Nikolay. Fundamentals of the theory and algorithms on graphs. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/978686.

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The textbook describes the main theoretical principles of graph theory, the main tasks to be solved using graph structures, and General methods of their solution and specific algorithms, with estimates of their complexity. I covered a lot of the examples given questions to test knowledge and tasks for independent decisions. Along with the control tasks to verify the theoretical training provided practical assignments to develop programs to study topics of graph theory. Meets the requirements of Federal state educational standards of higher education of the last generation. Designed for undergraduate and graduate programs, studying information technology, for in-depth training in analysis and design of systems of complex structure. Also the guide can be useful to specialists of the IT sphere in the study of algorithmic aspects of graph theory.
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Borschik, Natal'ya, and Aleksandr Tret'yakov. History of state and local government in Russia IX-XXI centuries. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1041557.

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The most important task of the Supreme bodies of state power and local administration in Russia is the strengthening and development of modern Russian federalism based on the constitutional relationship of the centre and subjects of Federation. The present textbook contains a set of materials for conducting lectures and practical exercises, background material, questions for self-students, etc. they are based on the author's development, some of which were used as assignments for seminars, essays, essays and other creative works within the framework of the teaching course "the Historical experience of state and local government in Russia." Meets the requirements of Federal state educational standards of higher education of the last generation. For students enrolled in training 46.03.02 "documentation studies and archival studies".
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Book chapters on the topic "Task assignment"

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Weyns, Danny. "Task Assignment." In Architecture-Based Design of Multi-Agent Systems, 123–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-01064-4_6.

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Takahara, Yasuhiko, and Mihajlo Mesarovic. "Task Assignment Coordination." In Organization Structure, 137–54. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4613-0213-1_8.

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Macdonald, Ian, Catherine Burke, and Karl Stewart. "Task Formulation and Assignment." In Systems Leadership, 187–98. Second edition. | Abingdon, Oxon; New York, NY : Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315178486-14.

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Moore, Brandon J., and Kevin M. Passino. "Task Assignment for Mobile Agents." In Cooperative Control of Distributed Multi-Agent Systems, 109–38. Chichester, UK: John Wiley & Sons, Ltd, 2007. http://dx.doi.org/10.1002/9780470724200.ch6.

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Liu, Shan Fan, and Mary Lou Soffa. "Parallel task assignment by graph partitioning." In PARLE '92 Parallel Architectures and Languages Europe, 965–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55599-4_144.

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Goyal, Rahul, Tushar Sharma, and Ritu Tiwari. "Priority Based Multi Robot Task Assignment." In Lecture Notes in Computer Science, 554–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30976-2_67.

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Orleans, Luis Fernando, Carlo Emmanoel de Oliveira, and Pedro Furtado. "Task Assignment on Parallel QoS Systems." In Web Information Systems Engineering – WISE 2007, 543–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76993-4_46.

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Gonçalves, Nelson, and João Sequeira. "Multirobot Task Assignment in Active Surveillance." In Progress in Artificial Intelligence, 310–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04686-5_26.

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Sipsas, Konstantinos, Nikolaos Nikolakis, and Sotiris Makris. "Dynamic Assembly Planning and Task Assignment." In Advanced Human-Robot Collaboration in Manufacturing, 183–210. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69178-3_8.

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Gong, Wei, Baoxian Zhang, and Cheng Li. "Task Assignment for Semi-opportunistic Mobile Crowdsensing." In Ad Hoc Networks, 3–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05888-3_1.

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Conference papers on the topic "Task assignment"

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Hajaj, Chen, and Yevgeniy Vorobeychik. "Adversarial Task Assignment." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/526.

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The problem of task assignment to workers is of long-standing fundamental importance. Examples of this include the classical problem of assigning computing tasks to nodes in a distributed computing environment, assigning jobs to robots, and crowdsourcing. Extensive research into this problem generally addresses important issues such as uncertainty and incentives. However, the problem of adversarial tampering with the task assignment process has not received as much attention. We are concerned with a particular adversarial setting in task assignment where an attacker may target a set of workers in order to prevent the tasks assigned to these workers from being completed. For the case when all tasks are homogeneous, we provide an efficient algorithm for computing the optimal assignment. When tasks are heterogeneous, we show that the adversarial assignment problem is NP-Hard, and present an algorithm for solving it approximately. Our theoretical results are accompanied by extensive simulation results showing the effectiveness of our algorithms.
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Tang, Feilong. "Optimal Complex Task Assignment in Service Crowdsourcing." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/217.

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Existing schemes cannot assign complex tasks to the most suitable workers because they either cannot measure skills quantitatively or do not consider assigning tasks to workers who are the most suitable but unavailable temporarily. In this paper, we investigate how to realize optimal complex task assignment. Firstly, we formulate the multiple-skill based task assignment problem in service crowdsourcing. We then propose a weighted multi-skill tree (WMST) to model multiple skills and their correlations. Next, we propose the acceptance expectation to uniformly measure the probabilities that different categories of workers will accept and complete specified tasks. Finally, we propose an acceptance-expectation-based task assignment (AE-TA) algorithm, which reserves tasks for the most suitable workers even unavailable temporarily. Comprehensive experimental results demonstrate that our WMST model and AE-TA algorithm significantly outperform related proposals.
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Yusuke Morihiro, Toshiyuki Miyamoto, and Sadatoshi Kumagai. "An initial task assignment method for tasks assignment and routing problem." In SICE Annual Conference 2007. IEEE, 2007. http://dx.doi.org/10.1109/sice.2007.4421039.

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Dang, Hung, Tuan Nguyen, and Hien To. "Maximum Complex Task Assignment." In International Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2539150.2539243.

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Xia, Jinfu, Yan Zhao, Guanfeng Liu, Jiajie Xu, Min Zhang, and Kai Zheng. "Profit-driven Task Assignment in Spatial Crowdsourcing." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/265.

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In Spatial Crowdsourcing (SC) systems, mobile users are enabled to perform spatio-temporal tasks by physically traveling to specified locations with the SC platforms. SC platforms manage the systems and recruit mobile users to contribute to the SC systems, whose commercial success depends on the profit attained from the task requesters. In order to maximize its profit, an SC platform needs an online management mechanism to assign the tasks to suitable workers. How to assign the tasks to workers more cost-effectively with the spatio-temporal constraints is one of the most difficult problems in SC. To deal with this challenge, we propose a novel Profit-driven Task Assignment (PTA) problem, which aims to maximize the profit of the platform. Specifically, we first establish a task reward pricing model with tasks' temporal constraints (i.e., expected completion time and deadline). Then we adopt an optimal algorithm based on tree decomposition to achieve the optimal task assignment and propose greedy algorithms to improve the computational efficiency. Finally, we conduct extensive experiments using real and synthetic datasets, verifying the practicability of our proposed methods.
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Earl, Matthew G., and Raffaello D’Andrea. "Phase Transitions in the Multi-Vehicle Task Assignment Problem." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-80512.

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We have developed real-time methods to synthesize cooperative strategies for the multi-vehicle task assignment problem in an adversarial environment. By introducing a set of tasks to be completed by the team of vehicles and a trajectory generation primitive for each vehicle, we formulate the multi-vehicle control problem as a task assignment problem. The continuous component of the problem is captured by the trajectory primitive, and the combinatorial component is captured by task assignment. We have developed an efficient branch and bound solver for the task assignment component of the problem. In this paper, we analyze the computational complexity of our solver with variations in parameters of the problem. We found a phase transition in the ratio of the maximum velocity of opposing vehicles, and we found a phase transition in the ratio of the number of vehicles per team. The results show that the task assignment problem is difficult to solve when the capabilities of the two teams are comparable and easy to solve when one team is more capable than the other.
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Jackson, Justin, Mariam Faied, Pierre Kabamba, and Anouck Girard. "Communication-constrained distributed task assignment." In 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011). IEEE, 2011. http://dx.doi.org/10.1109/cdc.2011.6160736.

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To, Hien. "Task assignment in spatial crowdsourcing." In SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/3003819.3003820.

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Shriyam, Shaurya, and Satyandra K. Gupta. "Task Assignment and Scheduling for Mobile Robot Teams." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-86007.

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Most complex missions comprise of spatially separated tasks which have to be finished using teams of mobile robots. The main challenges for planning such missions are forming effective coalitions among available robots and assigning them to tasks in such a way that the expected mission completion time is minimized. Our model allows task execution by a fraction of the assigned team even when the rest of the team has not yet arrived at the task location. We also allow tasks to be interrupted and robots of assigned teams to be rescheduled from an unfinished task to another task. We describe five different heuristic algorithms to compute schedules for all robots assigned to the mission. We compare them and analyze the computational performance of the best performing strategy. We also show how to handle uncertainty that may arise during traveling or task execution and then study the effect of varying uncertainty on the minimization of mission completion time.
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Ljubicic, Ivica, and Zeljka Car. "Competency based Task Assignment in Human Task Management Systems." In Parallel and Distributed Computing and Networks / Software Engineering. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.720-023.

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Reports on the topic "Task assignment"

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Harchol-Balter, Mor. Task Assignment With Unknown Duration. Fort Belvoir, VA: Defense Technical Information Center, August 1999. http://dx.doi.org/10.21236/ada368426.

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Basik, Kevin J. Small-Group Leader Assignment: Effects Across Different Degrees of Task Interdependence,. Fort Belvoir, VA: Defense Technical Information Center, July 1997. http://dx.doi.org/10.21236/ada327895.

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Shima, Tal, Pantelis Isaiah, and Yoav Gottlieb. Motion Planning and Task Assignment for Unmanned Aerial Vehicles Cooperating with Unattended Ground Sensors. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada619854.

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Schroeder, Bianca, and Mor Harchol-Balter. Evaluation of Task Assignment Policies for Supercomputing Servers: The Case for Load Unbalancing and Fairness. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada377091.

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Trabia, M. B., M. Kiley, J. Cardle, and M. Joseph. Report on task assignment No. 3 for the Waste Package Project; Parts A & B, ASME pressure vessel codes review for waste package application; Part C, Library search for reliability/failure rates data on low temperature low pressure piping, containers, and casks with long design lives. Office of Scientific and Technical Information (OSTI), July 1991. http://dx.doi.org/10.2172/138422.

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