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Journal articles on the topic 'Task assignment'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

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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5

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 â€œMâ€ tasks of distributed program to â€œNâ€ 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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Su, Jiafu, Jie Wang, Sheng Liu, Na Zhang, and Chi Li. "A Method for Efficient Task Assignment Based on the Satisfaction Degree of Knowledge." Complexity 2020 (September 3, 2020): 1–12. http://dx.doi.org/10.1155/2020/3543782.

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For the product R&D process, it is a challenge to effectively and reasonably assign tasks and estimate their execution time. This paper develops a method system for efficient task assignment in product R&D. The method system consists of three components: similar tasks identification, tasks’ execution time calculation, and task assignment model. The similar tasks identification component entails the retrieval of a similar task model to identify similar tasks. From the knowledge-based view, the tasks’ execution time calculation component uses the BP neural network to predict tasks’ execution time according to the previous similar tasks and the Task–Knowledge–Person (TKP) network. When constructing the BP neural network, the satisfaction degree of knowledge and the execution time are set as the input and output, respectively. Considering the uncertain factors associated with the whole R&D process, the task assignment model component serves as a robust optimization model to assign tasks. Then, an improved genetic algorithm is developed to solve the task assignment model. Finally, the results of numerical experiment are reported to validate the effectiveness of the proposed methods.

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12

Bruno, John, Edward G. Coffman, and Peter Downey. "Scheduling Independent Tasks to Minimize the Makespan on Identical Machines." Probability in the Engineering and Informational Sciences 9, no. 3 (July 1995): 447–56. http://dx.doi.org/10.1017/s026996480000396x.

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In this paper we consider scheduling n tasks on m parallel machines where the task processing times are i.i.d. random variables with a common distribution function F. Scheduling is done by an a priori assignment of tasks to machines. We show that if the distribution function F is a Pólya frequency function of order 2 (decreasing reverse hazard rate) then the assignment that attempts to place an equal number of tasks on each machine achieves the stochastically smallest makespan among all assignments. The condition embraces many important distributions, such as the gamma and truncated normal distributions. Assuming that the task processing times have a common density that is a Pólya frequency function of order 2 (increasing likelihood ratio), then we find that flatter schedules have stochastically smaller makespans in the sense of the “joint” likelihood ratio.

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13

KRISHNAMURTI, RAMESH, and BHAGIRATH NARAHARI. "OPTIMAL SUBCUBE ASSIGNMENT FOR PARTITIONABLE HYPERCUBES." Parallel Processing Letters 02, no. 01 (March 1992): 89–95. http://dx.doi.org/10.1142/s0129626492000210.

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This paper formulates and discusses a processor assignment problem arising in partitionable parallel architectures. A partitionable hypercube multiprocessor can simultaneously execute multiple tasks where each task is independently executed on a subcube. Given a p processor hypercube and n independent tasks, where a task can be assigned a subcube of any size, an assignment determines the size of the subcube — i.e., the number of processors — to be assigned to each task. The objective of our problem is to find the optimal assignment which minimizes the maximum execution time among all tasks. We present an O(n log p max { log log p, log n}) algorithm that determines an optimal assignment. This algorithm can be efficiently parallelized, on the p processor hypercube, to obtain an O((n/p) log p log 2(n log p)) parallel assignment algorithm.

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14

Costa, Joan E. Ricart I. "Managerial Task Assignment and Promotions." Econometrica 56, no. 2 (March 1988): 449. http://dx.doi.org/10.2307/1911081.

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15

Puschke, Kerstin. "Task assignment and organizational form." Journal of Economics 96, no. 2 (July 29, 2008): 149–68. http://dx.doi.org/10.1007/s00712-008-0033-z.

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16

Xiao, Yujie, and Dingxiong Zhang. "The Command Decision Method of Multiple UUV Cooperative Task Assignment Based on Contract Net Protocol." Journal of Systems Science and Information 4, no. 4 (August 25, 2016): 379–90. http://dx.doi.org/10.21078/jssi-2016-379-12.

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Abstract With the help of multiple UCAV cooperative task control model, the mathematical model of multiple UUV cooperative task control is made. Variables related to decision are broken into goals, guidelines and programs levels by Analytical Hierarchy Process (AHP), on this basis; the command decision of multiple UUV task assignment is achieved. The correctness of task allocation algorithm is verified by case analysis. Time calculation formulas for a task assignment are given. The changes of overall effectiveness in the process of task allocation are analyzed, the time changes of each sub task allocation time in one task assignment are analyzed, the time changes of the number of tasks and platforms respectively fixed in task allocation are also discussed.

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Hu, Xiaoxuan, Jing Cheng, and He Luo. "Task Assignment for Multi-UAV under Severe Uncertainty by Using Stochastic Multicriteria Acceptability Analysis." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/249825.

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This paper considers a task assignment problem for multiple unmanned aerial vehicles (UAVs). The UAVs are set to perform attack tasks on a collection of ground targets in a severe uncertain environment. The UAVs have different attack capabilities and are located at different positions. Each UAV should be assigned an attack task before the mission starts. Due to uncertain information, many criteria values essential to task assignment were random or fuzzy, and the weights of criteria were not precisely known. In this study, a novel task assignment approach based on stochastic Multicriteria acceptability analysis (SMAA) method was proposed to address this problem. The uncertainties in the criteria were analyzed, and a task assignment procedure was designed. The results of simulation experiments show that the proposed approach is useful for finding a satisfactory assignment under severe uncertain circumstances.

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Huang, Rong, An Ping Xiong, and Yang Zou. "Research on MapReduce Task Dynamic Balancing Strategy Based on File Label." Applied Mechanics and Materials 571-572 (June 2014): 17–21. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.17.

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MapReduce is one of the core framework of Hadoop, it’s computing performance has been widely concerned and researched. In heterogeneous environment, unreasonable map task assignments and inefficient resource utilization lead to multiple backup tasks and the job total execution time is poor.For these problems, this paper proposes a new map task assignment strategy, which is map task dynamic balancing strategy based on file label. The strategy marks on job according to the different types, estimates node computing capabilities and historical processing efficiency of each label task, ensures map task which was assigned can execute successfully. Experiments show that, the strategy can effectively reduce number of backup tasks in map phase, and to some extent optimize the total execution time of the job.

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Mondal, Ranjan Kumar, Payel Ray, Enakshmi Nandi, Biswajit.Biswas, Manas Kumar Sanyal, and Debabrata Sarddar. "Load Balancing of Unbalanced Assignment Problem With Hungarian Method." International Journal of Ambient Computing and Intelligence 10, no. 1 (January 2019): 46–60. http://dx.doi.org/10.4018/ijaci.2019010103.

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The cloud computing presents a type of assignments and systems which occupy distributed resources to execute a role in a distributed way. Cloud computing make use of the online systems on the web to assist the implementation of complicated assignments; that need huge-scale computation. It was said with the intention of in our living world; we can find it challenging to balance workloads of cloud computing among assignments (jobs or tasks) and systems (machines or nodes), so the majority of the time we have to promote a condition to unbalanced assignment problems (unequal task allocations). The present article submits a new technique to solve the unequal task allocation problems. The technique is offered in an algorithmic model and put into practice on the several groups of input to investigate the presentation and usefulness of the works. An evaluation is prepared with the presented approach. It makes sure that the proposed approach provides a better outcome by comparing with some other existing algorithms.

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Lujak, Marin, Stefano Giordani, Andrea Omicini, and Sascha Ossowski. "Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation." Complexity 2020 (July 16, 2020): 1–21. http://dx.doi.org/10.1155/2020/1047369.

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One of the major challenges in the coordination of large, open, collaborative, and commercial vehicle fleets is dynamic task allocation. Self-concerned individually rational vehicle drivers have both local and global objectives, which require coordination using some fair and efficient task allocation method. In this paper, we review the literature on scalable and dynamic task allocation focusing on deterministic and dynamic two-dimensional linear assignment problems. We focus on multiagent system representation of open vehicle fleets where dynamically appearing vehicles are represented by software agents that should be allocated to a set of dynamically appearing tasks. We give a comparison and critical analysis of recent research results focusing on centralized, distributed, and decentralized solution approaches. Moreover, we propose mathematical models for dynamic versions of the following assignment problems well known in combinatorial optimization: the assignment problem, bottleneck assignment problem, fair matching problem, dynamic minimum deviation assignment problem, Σk-assignment problem, the semiassignment problem, the assignment problem with side constraints, and the assignment problem while recognizing agent qualification; all while considering the main aspect of open vehicle fleets: random arrival of tasks and vehicles (agents) that may become available after assisting previous tasks or by participating in the fleet at times based on individual interest.

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Meddeber, Meriem, and Belabbas Yagoubi. "Dynamic Dependent Tasks Assignment for Grid Computing." International Journal of Grid and High Performance Computing 3, no. 2 (April 2011): 44–58. http://dx.doi.org/10.4018/jghpc.2011040104.

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A computational grid is a widespread computing environment that provides huge computational power for large-scale distributed applications. One of the most important issues in such an environment is resource management. Task assignment as a part of resource management has a considerable effect on the grid middleware performance. In grid computing, task execution time is dependent on the machine to which it is assigned, and task precedence constraints are represented by a directed acyclic graph. This paper proposes a hybrid assignment strategy of dependent tasks in Grids which integrate static and dynamic assignment technologies. Grid computing is considered a set of clusters formed by a set of computing elements and a cluster manager. The main objective is to arrive at a method of task assignment that could achieve minimum response time and reduce the transfer cost, inducing by the tasks transfer respecting the dependency constraints.

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Hettiachchi, Danula, Lachie Hayes, Jorge Goncalves, and Vassilis Kostakos. "Team Dynamics in Hospital Workflows: An Exploratory Study of a Smartphone Task Manager." JMIR Medical Informatics 9, no. 8 (August 16, 2021): e28245. http://dx.doi.org/10.2196/28245.

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Background Although convenient and reliable modern messaging apps like WhatsApp enable efficient communication among hospital staff, hospitals are now pivoting toward purpose-built structured communication apps for various reasons, including security and privacy concerns. However, there is limited understanding of how we can examine and improve hospital workflows using the data collected through such apps as an alternative to costly and challenging research methods like ethnography and patient record analysis. Objective We seek to identify whether the structure of the collected communication data provides insights into hospitals’ workflows. Our analysis also aims to identify ways in which task management platforms can be improved and designed to better support clinical workflows. Methods We present an exploratory analysis of clinical task records collected over 22 months through a smartphone app that enables structured communication between staff to manage and execute clinical workflows. We collected over 300,000 task records between July 2018 and May 2020 completed by staff members including doctors, nurses, and pharmacists across all wards in an Australian hospital. Results We show that important insights into how teams function in a clinical setting can be readily drawn from task assignment data. Our analysis indicates that predefined labels such as urgency and task type are important and impact how tasks are accepted and completed. Our results show that both task sent-to-accepted (P<.001) and sent-to-completed (P<.001) times are significantly higher for routine tasks when compared to urgent tasks. We also show how task acceptance varies across teams and roles and that internal tasks are more efficiently managed than external tasks, possibly due to increased trust among team members. For example, task sent-to-accepted time (minutes) is significantly higher (P<.001) for external assignments (mean 22.10, SD 91.45) when compared to internal assignments (mean 19.03, SD 82.66). Conclusions Smartphone-based task assignment apps can provide unique insights into team dynamics in clinical settings. These insights can be used to further improve how well these systems support clinical work and staff.

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Agrawal, Amrit, and Pranay Chaudhuri. "An Algorithm for Task Scheduling in Heterogeneous Distributed Systems Using Task Duplication." International Journal of Grid and High Performance Computing 3, no. 1 (January 2011): 89–97. http://dx.doi.org/10.4018/jghpc.2011010105.

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Task scheduling in heterogeneous parallel and distributed computing environment is a challenging problem. Applications identified by parallel tasks can be represented by directed-acyclic graphs (DAGs). Scheduling refers to the assignment of these parallel tasks on a set of bounded heterogeneous processors connected by high speed networks. Since task assignment is an NP-complete problem, instead of finding an exact solution, scheduling algorithms are developed based on heuristics, with the primary goal of minimizing the overall execution time of the application or schedule length. In this paper, the overall execution time (schedule length) of the tasks is reduced using task duplication on top of the Critical-Path-On-a-Processor (CPOP) algorithm.

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Gołda, Ilona Jacyna, Mariusz Izdebski, and Askoldas Podviezko. "ASSESSMENT OF EFFICIENCY OF ASSIGNMENT OF VEHICLES TO TASKS IN SUPPLY CHAINS: A CASE STUDY OF A MUNICIPAL COMPANY." Transport 32, no. 3 (January 17, 2017): 243–51. http://dx.doi.org/10.3846/16484142.2016.1275040.

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The main purpose of the paper is to present criteria of efficiency of assignment of vehicles to tasks at municipal companies, which collect garbage from city inhabitants. Three types of criteria are introduced in the paper: garbage collection time, length of route allocation, and utilization of resources. A two-stage method of optimization of taskroutes is proposed. It generates tasks at the first stage and assigns vehicles to the tasks at the second stage. At municipal companies that are responsible for garbage, collection tasks are not pre-defined, and consequently tasks must be designated before the workday. The proposed method is based on genetic algorithm, which is used for the purpose of optimization of the assignment problem. The obtained by the algorithm optimal assignment is compared with assignments obtained in the random way. Criteria of evaluation of efficiency of the obtained route of different mutually conflicting dimensions were introduced, such as is task realization time, distances travelled on particular routes, and number of vehicles involved in garbage collection. Efficiency of the obtained assignment appeared to be sufficiently good.

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Ahmadi, Ahmadi, Benny Sukandari, and Agus Mahrowi. "THE WARSHIP ASSIGNMENT SCHEDULE USING INTEGER PROGRAMMING MODEL." JOURNAL ASRO 10, no. 3 (October 31, 2019): 49. http://dx.doi.org/10.37875/asro.v10i3.150.

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Scheduling is an assignment activity that deals with constraints. A number of events can occur in a period of time and location so that objective functions as close as possible can be fulfilled. In the hierarchy of decision making, scheduling is the last step before the start of an operation. Scheduling warship assignments in Kolinlamil are an interesting topic to discuss and find solutions to using mathematical methods. The process of the Kolinlamil warship assignment schedule was carried out to produce an annual shipbuilding schedule. This process not only requires fast follow-up, but also requires systematic and rigorous steps. Where the assignment schedule is a fairly complex combinatorial problem. While making the assignment schedule that is applied at this time is considered less accurate because it calculates the conventional method. The process of warship assignment schedule in this study using the Integer Programming model aims to obtain alternative scheduling operations. The schedule observed was 13 warships in carrying out N operations for 1 year (52 weeks). This research begins with determining the decision variables and limitations that existing constraints. Hard constraints include: maintenance schedule, time and duration of each task, warship class assigned to the task and the number of executing warships per task. While soft constraints are how long the warship performs its tasks in a row. The mathematical formulation of the Integer Programming model created consists of three indicator, one decision variables, two measuring parameters and five constraint functions. Furthermore, determining the best scheduling alternatives is completed using the Microsoft Exel Solver computing program.Keywords: Scheduling, Integer Programming, Solver.

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Zhang, Hao, Yao Ma, and Masashi Sugiyama. "Bandit-Based Task Assignment for Heterogeneous Crowdsourcing." Neural Computation 27, no. 11 (November 2015): 2447–75. http://dx.doi.org/10.1162/neco_a_00782.

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We consider a task assignment problem in crowdsourcing, which is aimed at collecting as many reliable labels as possible within a limited budget. A challenge in this scenario is how to cope with the diversity of tasks and the task-dependent reliability of workers; for example, a worker may be good at recognizing the names of sports teams but not be familiar with cosmetics brands. We refer to this practical setting as heterogeneous crowdsourcing. In this letter, we propose a contextual bandit formulation for task assignment in heterogeneous crowdsourcing that is able to deal with the exploration-exploitation trade-off in worker selection. We also theoretically investigate the regret bounds for the proposed method and demonstrate its practical usefulness experimentally.

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KAYA, KAMER, and BORA UÇAR. "EXACT ALGORITHMS FOR A TASK ASSIGNMENT PROBLEM." Parallel Processing Letters 19, no. 03 (September 2009): 451–65. http://dx.doi.org/10.1142/s012962640900033x.

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We consider the following task assignment problem. Communicating tasks are to be assigned to heterogeneous processors interconnected with a heterogeneous network. The objective is to minimize the total sum of the execution and communication costs. The problem is NP-hard. We present an exact algorithm based on the well-known A* search. We report simulation results over a wide range of parameters where the largest solved instance contains about three hundred tasks to be assigned to eight processors.

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Bracci, A., M. Innocenti, and L. Pollini. "Cooperative Task Assignment Using Dynamic Ranking." IFAC Proceedings Volumes 41, no. 2 (2008): 5712–17. http://dx.doi.org/10.3182/20080706-5-kr-1001.00963.

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Siva Ram Murthy, C., and V. Rajaraman. "Task assignment in a multiprocessor system." Microprocessing and Microprogramming 26, no. 1 (March 1989): 63–71. http://dx.doi.org/10.1016/0165-6074(89)90282-2.

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Broberg, James, Zahir Tari, and Panlop Zeephongsekul. "Task assignment with work-conserving migration." Parallel Computing 32, no. 11-12 (December 2006): 808–30. http://dx.doi.org/10.1016/j.parco.2006.09.005.

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Devereux, Paul J. "Task Assignment over the Business Cycle." Journal of Labor Economics 18, no. 1 (January 2000): 98–124. http://dx.doi.org/10.1086/209952.

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Li, Yongkui, Yujie Lu, Dongyu Li, and Liang Ma. "Metanetwork Analysis for Project Task Assignment." Journal of Construction Engineering and Management 141, no. 12 (December 2015): 04015044. http://dx.doi.org/10.1061/(asce)co.1943-7862.0001019.

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33

Gutjahr, W. J., M. Hitz, and T. A. Mueck. "Task assignment in Cayley interconnection topologies." Parallel Computing 23, no. 10 (October 1997): 1429–60. http://dx.doi.org/10.1016/s0167-8191(97)89285-x.

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SEO, KYUNG-RYONG, and KYU-HO PARK. "TASK ASSIGNMENT IN HOST-SATELLITE SYSTEMS." Journal of Circuits, Systems and Computers 06, no. 03 (June 1996): 213–25. http://dx.doi.org/10.1142/s0218126696000170.

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This paper deals with the problem of assigning task modules of a program over a multiple computer system such that the sum of execution and communication costs is minimized. If the number of processors is two, this problem can be solved efficiently using the network flow approach pioneered by Stone.13 However, the general n-processor problem (n>3) in a fully connected system is known to be NP-complete.14 A host-satellite system considered in this paper is composed of a powerful host processor p0 and N homogeneous satellite processors pk’s, 1≤k≤N, in which each satellite processor pk is connected to the host processor p0 through a communication link. When any two satellite processors are to communicate with each other, the host processor p0 must participate in the communication. Therefore, the interprocessor communication cost per unit of information transferred between any two satellite processors is twice as much as that between a satellite processor and the host processor p0. In this paper, we propose an algorithm which finds an optimal assignment on a host-satellite system in polynomial time. A task assignment problem for a host-satellite system is first transformed into a network flow problem, and then solved by applying the well known network flow algorithm in time no worse than O(NM3), where N and M are the number of satellites and the number of modules, respectively.

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Radojković, Petar, Vladimir Čakarević, Miquel Moretó, Javier Verdú, Alex Pajuelo, Francisco J. Cazorla, Mario Nemirovsky, and Mateo Valero. "Optimal task assignment in multithreaded processors." ACM SIGARCH Computer Architecture News 40, no. 1 (April 18, 2012): 235–48. http://dx.doi.org/10.1145/2189750.2151002.

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Moore, B. J., and K. M. Passino. "Distributed Task Assignment for Mobile Agents." IEEE Transactions on Automatic Control 52, no. 4 (April 2007): 749–53. http://dx.doi.org/10.1109/tac.2007.894545.

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Ucar, Bora, Cevdet Aykanat, Kamer Kaya, and Murat Ikinci. "Task assignment in heterogeneous computing systems." Journal of Parallel and Distributed Computing 66, no. 1 (January 2006): 32–46. http://dx.doi.org/10.1016/j.jpdc.2005.06.014.

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Tong, Yongxin, and Zimu Zhou. "Dynamic task assignment in spatial crowdsourcing." SIGSPATIAL Special 10, no. 2 (November 13, 2018): 18–25. http://dx.doi.org/10.1145/3292390.3292395.

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Chen, Zhao, Peng Cheng, Lei Chen, Xuemin Lin, and Cyrus Shahabi. "Fair task assignment in spatial crowdsourcing." Proceedings of the VLDB Endowment 13, no. 12 (August 2020): 2479–92. http://dx.doi.org/10.14778/3407790.3407839.

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Zhang, Yonglong, Haiyan Qin, Bin Li, Jin Wang, Sungyoung Lee, and Zhiqiu Huang. "Truthful mechanism for crowdsourcing task assignment." Tsinghua Science and Technology 23, no. 6 (December 2018): 645–59. http://dx.doi.org/10.26599/tst.2018.9010064.

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41

Raravi, Gurulingesh, and Vincent Nélis. "Task Assignment Algorithms for Heterogeneous Multiprocessors." ACM Transactions on Embedded Computing Systems 13, no. 5s (December 15, 2014): 1–26. http://dx.doi.org/10.1145/2660494.

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42

De Paola, Maria, and Vincenzo Scoppa. "Task assignment, incentives and technological factors." Managerial and Decision Economics 30, no. 1 (January 2009): 43–55. http://dx.doi.org/10.1002/mde.1434.

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Radojković, Petar, Vladimir Čakarević, Miquel Moretó, Javier Verdú, Alex Pajuelo, Francisco J. Cazorla, Mario Nemirovsky, and Mateo Valero. "Optimal task assignment in multithreaded processors." ACM SIGPLAN Notices 47, no. 4 (June 2012): 235–48. http://dx.doi.org/10.1145/2248487.2151002.

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HSU, CHIUN-CHIEH, SHENG-DE WANG, and TE-SON KUO. "Task assignment in distributed computing systems." International Journal of Systems Science 21, no. 12 (December 1990): 2425–40. http://dx.doi.org/10.1080/00207729008910562.

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Cheol-Hoon Lee and K. G. Shin. "Optimal task assignment in homogeneous networks." IEEE Transactions on Parallel and Distributed Systems 8, no. 2 (1997): 119–29. http://dx.doi.org/10.1109/71.577254.

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Daido, Kohei, Kimiyuki Morita, Takeshi Murooka, and Hiromasa Ogawa. "Task assignment under agent loss aversion." Economics Letters 121, no. 1 (October 2013): 35–38. http://dx.doi.org/10.1016/j.econlet.2013.06.040.

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Beloglazov, Denis Aleksandrovich, Valery Ivanovich Finaev, Victor Vladimirovich Soloviev, and Elena Nikolaevna Pavlenko. "Methods Research and Software Development for Parameters Formalization of the Assignment Task Applicable to the Target Distribution." Journal of Robotics 2020 (February 28, 2020): 1–11. http://dx.doi.org/10.1155/2020/6926291.

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The paper discusses the solution of the assignment task between two groups of mobile (MR) objects. The assignment task is to determine the purpose of MR to each other when playing football. This article analyzes well-known works devoted to the tasks of assignments, and the relevance of research in this area is noted. The task of target distribution is successfully solved with a small degree of uncertainty. The urgency of further research to solve the problem of target distribution in conditions of uncertainty is substantiated. The model of target assignment is considered. The basis of the model is a bipartite graph. The vertices of a bipartite graph are defined by the properties of MR. One group of MR is assigned red color and the other one group is blue. The properties of the mobile objects are formally determined by the membership grades given by the experts. Identification of a bipartite graph is a solution to the assignment problem. When the problem of target distribution on a bipartite graph is solved, the criterion of the maximum degree of bipartite is applied. Formulas are given to determine the degree of bipartite, taking into account the number of vertices in the graph and the weight of each edge in two fractions of the graph. The study of the target distribution model was carried out by the simulation method. A program is developed to solve the problem of assignments. Properties and degrees of belonging of properties of red and blue MR are set for carrying out researches. The program allows you to produce research and under equality of red and blue MR, and under their inequality. The analysis of modeling results is carried out.

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Luo, Lingzhi, Nilanjan Chakraborty, and Katia Sycara. "Distributed Algorithms for Multirobot Task Assignment With Task Deadline Constraints." IEEE Transactions on Automation Science and Engineering 12, no. 3 (July 2015): 876–88. http://dx.doi.org/10.1109/tase.2015.2438032.

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Miles, Jeffrey A. "The Influence of Goal Commitment and Partner Status on Team Assignment Outcomes." Psychological Reports 123, no. 3 (January 17, 2019): 844–71. http://dx.doi.org/10.1177/0033294118821299.

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The present study replicated and extended research on the influence of team assignment methods on task performance and fairness perceptions. This study examined the influence of team assignment methods, goal commitment, and partner status on team member performance and fairness perceptions in a laboratory setting. The assignment conditions were comprised of three variables: assignment method (random, self-decision, and ability), performer status (assigned or unassigned), and partner status (unassigned team member stayed or left during task performance). A significant interaction was found between assignment method and performer status when the unassigned team member left during task performance, but not when the unassigned team member stayed. Random and self-decision assignment methods resulted in higher levels of goal commitment and task performance than did ability-based assignment conditions. Lastly, goal commitment was found to mediate the relationship between assignment method and task performance. The implications of these findings for the task performance and organizational justice literatures, as well as for managers in general, are discussed.

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Xu, Ling, Jianzhong Qiao, Shukuan Lin, and Ruihua Qi. "Task Assignment Algorithm Based on Trust in Volunteer Computing Platforms." Information 10, no. 7 (July 23, 2019): 244. http://dx.doi.org/10.3390/info10070244.

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In volunteer computing (VC), the expected availability time and the actual availability time provided by volunteer nodes (VNs) are usually inconsistent. Scheduling tasks with precedence constraints in VC under this situation is a new challenge. In this paper, we propose two novel task assignment algorithms to minimize completion time (makespan) by a flexible task assignment. Firstly, this paper proposes a reliability model, which uses a simple fuzzy model to predict the time interval provided by a VN. This reliability model can reduce inconsistencies between the expected availability time and actual availability time. Secondly, based on the reliability model, this paper proposes an algorithm called EFTT (Earliest Finish Task based on Trust, EFTT), which can minimize makespan. However, EFTT may induce resource waste in task assignment. To make full use of computing resources and reduce task segmentation rate, an algorithm IEFTT (improved earliest finish task based on trust, IEFTT) is further proposed. Finally, experimental results verify the efficiency of the proposed algorithms.

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