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Artykuły w czasopismach na temat „Task assignment”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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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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Wang, Wenfei, Maolong Lv, Le Ru, Bo Lu, Shiguang Hu, and Xinlong Chang. "Multi-UAV Unbalanced Targets Coordinated Dynamic Task Allocation in Phases." Aerospace 9, no. 9 (September 1, 2022): 491. http://dx.doi.org/10.3390/aerospace9090491.

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Unmanned aerial vehicles (UAVs) can be used in swarms to achieve multiple tasks cooperatively. Multi-UAV and multi-target cooperative task assignments are difficult. To solve the problem of unbalanced, phased, cooperative assignment between UAVs and tasks, we establish an unbalanced, phased task assignment model that considers the constraints of task execution, time, and target task execution demand. Based on an improved consensus-based bundle algorithm (CBBA), we propose a two-tier task bidding mechanism. According to dynamic changes in new tasks, we study a dynamic assignment strategy and propose a mechanism based on task continuity adjustment and time windows. Finally, a simulation experiment is used to verify the feasibility and effectiveness of the proposed allocation method in multi-UAV target assignment scenarios. The results show that the dynamic task assignment strategy can efficiently assign random new tasks as they arise.

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3

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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He, Xiaoxue, Yubo Wang, Xu Zhao, Tiancong Huang, and Yantao Yu. "Task Assignment and Path Planning Mechanism Based on Grade-Matching Degree and Task Similarity in Participatory Crowdsensing." Sensors 24, no. 2 (January 19, 2024): 651. http://dx.doi.org/10.3390/s24020651.

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Participatory crowdsensing (PCS) is an innovative data sensing paradigm that leverages the sensors carried in mobile devices to collect large-scale environmental information and personal behavioral data with the user’s participation. In PCS, task assignment and path planning pose complex challenges. Previous studies have only focused on the assignment of individual tasks, neglecting or overlooking the associations between tasks. In practice, users often tend to execute similar tasks when choosing assignments. Additionally, users frequently engage in tasks that do not match their abilities, leading to poor task quality or resource wastage. This paper introduces a multi-task assignment and path-planning problem (MTAPP), which defines utility as the ratio of a user’s profit to the time spent on task execution. The optimization goal of MATPP is to maximize the utility of all users in the context of task assignment, allocate a set of task locations to a group of workers, and generate execution paths. To solve the MATPP, this study proposes a grade-matching degree and similarity-based mechanism (GSBM) in which the grade-matching degree determines the user’s income. It also establishes a mathematical model, based on similarity, to investigate the impact of task similarity on user task completion. Finally, an improved ant colony optimization (IACO) algorithm, combining the ant colony and greedy algorithms, is employed to maximize total utility. The simulation results demonstrate its superior performance in terms of task coverage, average task completion rate, user profits, and task assignment rationality compared to other algorithms.

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Lujak, Marin, Alessio Salvatore, Alberto Fernández, Stefano Giordani, and Kendal Cousy. "How to fairly and efficiently assign tasks in individually rational agents’ coalitions? Models and fairness measures." Computer Science and Information Systems, no. 00 (2023): 75. http://dx.doi.org/10.2298/csis230119075l.

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An individually rational agent will participate in a multiagent coalition if the participation, given available information and knowledge, brings a payoff that is at least as high as the one achieved by not participating. Since agents? performance and skills may vary from task to task, the decisions about individual agent-task assignment will determine the overall performance of the coalition. Maximising the efficiency of the one-on-one assignment of tasks to agents corresponds to the conventional linear sum assignment problem, which considers efficiency as the sum of the costs or benefits of individual agent-task assignments obtained by the coalition as a whole. This approach may be unfair since it does not explicitly consider fairness and, thus, is unsuitable for individually rational agents? coalitions. In this paper, we propose two new assignment models that balance efficiency and fairness in task assignment and study the utilitarian, egalitarian, and Nash social welfare for task assignment in individually rational agents? coalitions. Since fairness is a relatively abstract term that can be difficult to quantify, we propose three new fairness measures based on equity and equality and use them to compare the newly proposed models. Through functional examples, we show that a reasonable trade-off between efficiency and fairness in task assignment is possible through the use of the proposed models.

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6

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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7

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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8

Mir, Shantona, and Sukanto Roy. "Assignment Again! Investigating the Psychological Perspectives of Assignment and Assignment Deadlines among Tertiary Level Students." English Language Teaching Perspectives 9, no. 1-2 (August 13, 2024): 117–30. http://dx.doi.org/10.3126/eltp.v9i1-2.68725.

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This paper aims to study university students' psychological perspectives on assignment deadlines. The purpose of this study is to investigate the issues faced by students before assignment submission and their relation to the education system, programme of study, personalities of teachers, and the importance of assignments. All in all, this paper delves into students' psychological barriers regarding assignments and the problems teachers face while assigning assignments. A mixed method is applied to conduct the research. The quantitative data has been collected using a survey among students from 8 different universities, and the qualitative data has employed interviews with teachers from 2 different universities. Procrastination, task management, pressure, and stress have been found to play the greatest role in facilitating the obstacles students face while completing an academic task. From the collected data, it is also recorded that students take into account multiple factors when it comes to assignments and their deadlines, i.e., not enough practical knowledge gain, the teaching style of the teacher, length of the assignment, time allocation, methods used in different programme of study, lack of interest in a course, demotivation, discouragement, and hard courses. A common view among the interviewees is that teachers’ lack of exposure to technology and deficient knowledge of teaching may be the reasons that students develop such attitudes toward assignments. In addition, this paper also addresses feasible solutions and recommendations to this very issue for the betterment of students and enhancing the education system.

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9

Li, Xin, Wenyang Gan, Pang Wen, and Daqi Zhu. "Multi-AUV Kinematic Task Assignment Based on Self-Organizing Map Neural Network and Dubins Path Generator." Sensors 24, no. 19 (September 30, 2024): 6345. http://dx.doi.org/10.3390/s24196345.

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To deal with the task assignment problem of multi-AUV systems under kinematic constraints, which means steering capability constraints for underactuated AUVs or other vehicles likely, an improved task assignment algorithm is proposed combining the Dubins Path algorithm with improved SOM neural network algorithm. At first, the aimed tasks are assigned to the AUVs by the improved SOM neural network method based on workload balance and neighborhood function. When there exists kinematic constraints or obstacles which may cause failure of trajectory planning, task re-assignment will be implemented by changing the weights of SOM neurals, until the AUVs can have paths to reach all the targets. Then, the Dubins paths are generated in several limited cases. The AUV’s yaw angle is limited, which results in new assignments to the targets. Computation flow is designed so that the algorithm in MATLAB and Python can realize the path planning to multiple targets. Finally, simulation results prove that the proposed algorithm can effectively accomplish the task assignment task for a multi-AUV system.

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10

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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11

Shuan-Jun Song, Shuan-Jun Song, Long-Guang Peng Shuan-Jun Song, Jie Zhang Long-Guang Peng, and Zhen Liu Jie Zhang. "A Dynamic Task Assignment Optimization Method for Multi-AGV System Based on Genetic Algorithm." 電腦學刊 34, no. 3 (June 2023): 093–108. http://dx.doi.org/10.53106/199115992023063403007.

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<p>Aiming at the influence of AGV without considering the working state on task assignment decision in multi-AGV system task assignment, a dynamic task assignment decision method with task completion prediction based on genetic algorithm. When assigning the arrived tasks at each stage, the decision method brings the working AGVs and the idle AGVs into the set of schedulable vehicles at the same time, which expands the scope of the optimal decision, makes the available AGV resources more fully mobilized in the dynamic scheduling process, and improves the efficiency of the whole scheduling system. First, this paper establishes a prediction model for task completion. On this basis, the task assignment decision model of multi-AGV system based on task completion prediction is established, and the coding, fitness function and genetic operation of the genetic algorithm suitable for this problem are designed. Finally, a univariate factor analysis is carried out on the task assignment time interval and the number of AGVs by using an example, which verifies the effectiveness of the task assignment strategy of the multi-AGV system based on task completion prediction. The results show that the genetic algorithm can better solve the task assignment problem with task completion prediction, and can schedule the available AGV resources to a greater extent, which effectively increase the number of tasks completed by the multi-AGV system in one production cycle.</p> <p>&nbsp;</p>

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12

Shah, Julie, Patrick Conrad, and Brian Williams. "Fast Distributed Multi-agent Plan Execution with Dynamic Task Assignment and Scheduling." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 289–96. http://dx.doi.org/10.1609/icaps.v19i1.13362.

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An essential quality of a good partner is her responsiveness to other team members. Recent work in dynamic plan execution exhibits elements of this quality through the ability to adapt to the temporal uncertainties of others agents and the environment. However, a good teammate also has the ability to adapt on-the-fly through task assignment. We generalize the framework of dynamic execution to perform plan execution with dynamic task assignment as well as scheduling.This paper introduces Chaski, a multi-agent executive for scheduling temporal plans with online task assignment. Chaski enables an agent to dynamically update its plan in response to disturbances in task assignment and the schedule of other agents. The agent then uses the updated plan to choose, schedule and execute actions that are guaranteed to be temporally consistent and logically valid within the multi-agent plan. Chaski is made efficient through an incremental algorithm that compactly encodes all scheduling policies for all possible task assignments. We apply Chaski to perform multi-manipulator coordination using two Barrett Arms within the authors' hardware testbed. We empirically demonstrate up to one order of magnitude improvements in execution latency and solution compactness compared to prior art.

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Diana, Ulfa, and Rifky Fauzi. "PEMBAGIAN TUGAS APEL PAGI MINGGUAN PEGAWAI INSTANSI XYZ DENGAN MENGGUNAKAN PEWARNAAN GRAF BESERTA ANALISIS BEBAN PENUGASAN." Al-Aqlu: Jurnal Matematika, Teknik dan Sains 3, no. 1 (January 18, 2025): 31–42. https://doi.org/10.59896/aqlu.v3i1.60.

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This study addresses the issue of the weekly morning roll call duty assignment at XYZ Agency. The allocation of morning roll call duties at XYZ Agency has been incidental, leading to uneven task assignments for employees. The author proposes a solution to this problem by implementing the Welch-Powell Algorithm on Graph Coloring to determine the allocation of morning roll call duties for XYZ Agency employees. This is achieved by representing each employee as a point, and the edges represent job similarities. The graph coloring results in this study produce 4 colors: red, green, blue, and purple, with connected points having different colors. The findings reveal that 20 employees participate in the roll call duty each month. The duty assignments are then made for six months and compiled into a task assignment table. Based on descriptive statistical results, each employee receives an average assignment of 4.68 times with a standard deviation of 1.38. Furthermore, the Levene's Test is employed to ensure the equality of workload among different employee job groups. The results indicate differences in the assignment workload among job groups

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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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Manoj, K., Krishna Raj, and G. Chaitanya Bharathi. "Employee Task Assignment Android Application." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (December 10, 2024): 1–6. https://doi.org/10.55041/ijsrem39596.

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An Android-based Employee Tracking System was created to monitor all employee activities. This system tracks scheduling and time-off requests, which are crucial for managers to determine when employees are expected to be in the office or working remotely. The system provides a convenient way for managers to oversee their team’s performance via mobile phones. It was developed using the JAVA programming language, with data being stored in an SQLite database. The design followed an Object-Oriented Analysis and Design methodology, incorporating a structured, iterative approach. Data was gathered through document reviews and field methods, with analytical tools like bar charts applied to analyze the findings. The implemented system successfully improved productivity, reduced costs, and enabled instant access to employee attendance records.

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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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Hettiachchi, Danula, Vassilis Kostakos, and Jorge Goncalves. "A Survey on Task Assignment in Crowdsourcing." ACM Computing Surveys 55, no. 3 (April 30, 2023): 1–35. http://dx.doi.org/10.1145/3494522.

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Quality improvement methods are essential to gathering high-quality crowdsourced data, both for research and industry applications. A popular and broadly applicable method is task assignment that dynamically adjusts crowd workflow parameters. In this survey, we review task assignment methods that address: heterogeneous task assignment, question assignment, and plurality problems in crowdsourcing. We discuss and contrast how these methods estimate worker performance, and highlight potential challenges in their implementation. Finally, we discuss future research directions for task assignment methods, and how crowdsourcing platforms and other stakeholders can benefit from them.

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Sun, Cheng, Yuwen Yao, and Enhui Zheng. "Enhancing Unmanned Aerial Vehicle Task Assignment with the Adaptive Sampling-Based Task Rationality Review Algorithm." Drones 8, no. 9 (August 24, 2024): 422. http://dx.doi.org/10.3390/drones8090422.

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As the application areas of unmanned aerial vehicles (UAVs) continue to expand, the importance of UAV task allocation becomes increasingly evident. A highly effective and efficient UAV task assignment method can significantly enhance the quality of task completion. However, traditional heuristic algorithms often perform poorly in complex and dynamic environments, and existing auction-based algorithms typically fail to ensure optimal assignment results. Therefore, this paper proposes a more rigorous and comprehensive mathematical model for UAV task assignment. By introducing task path decision variables, we achieve a mathematical description of UAV task paths and propose collaborative action constraints. To balance the benefits and efficiency of task assignment, we introduce a novel method: the Adaptive Sampling-Based Task Rationality Review Algorithm (ASTRRA). In the ASTRRA, to address the issue of high-value tasks being easily overlooked when the sampling probability decreases, we propose an adaptive sampling strategy. This strategy increases the sampling probability of high-value targets, ensuring a balance between computational efficiency and maximizing task value. To handle the coherence issues in UAV task paths, we propose a task review and classification method. This method involves reviewing issues in UAV task paths and conducting classified independent auctions, thereby improving the overall task assignment value. Additionally, to resolve the crossover problems between UAV task paths, we introduce a crossover path exchange strategy, further optimizing the task assignment scheme and enhancing the overall value. Experimental results demonstrate that the ASTRRA exhibits excellent performance across various task scales and dynamic scenarios, showing strong robustness and effectively improving task assignment outcomes.

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Wagner, Glenn, Howie Choset, and Nora Ayanian. "Subdimensional Expansion and Optimal Task Reassignment." Proceedings of the International Symposium on Combinatorial Search 3, no. 1 (August 20, 2021): 177–78. http://dx.doi.org/10.1609/socs.v3i1.18261.

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Multirobot path planning and task assignment are traditionally treated separately, however task assignment can greatly impact the difficulty of the path planning problem, and the ultimate quality of solution is dependent upon both. We introduce task reassignment, an approach to optimally solving the coupled task assignment and path planning problems. We show that task reassignment improves solution quality, and reduces planning time in some situations.

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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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Aeni, Nur, Vivit Rosmayanti, and Tuti Wijayanti. "Threats to the Execution of the English Presentation Task for University Students." Jadila: Journal of Development and Innovation in Language and Literature Education 5, no. 1 (March 2, 2025): 61–78. https://doi.org/10.52760/jadila.v5i1.804.

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English presentation tasks for university students are designed to enhance communication skills, boost confidence, and improve language proficiency. These tasks involve researching topic, organizing content, and delivering it effectively to an audience. However, not all students can deliver it confidently. The purpose of the study was to find out what elements affect EFL learners' comprehension of the materials when English presenting assignments are implemented. To triangulate the data for this qualitative descriptive study, interviews and classroom observations were used. The researcher employed methods such as data reduction, data display, and conclusion drafting in order to analyze the data. The findings demonstrated that giving students English presenting assignments improved their comprehension of the subject matter. To put it another way, the tasks helped students understand the topic better. However, the assignment's ability to completely improve comprehension was limited by impediments such students' worry, psychological hurdles, time limits, and procrastination. Furthermore, students stated that although the assignment was helpful, these challenges frequently affected their confidence and participation in the presentations.

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Holder, Joshua, Natasha Jaques, and Mehran Mesbahi. "Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (April 11, 2025): 26516–24. https://doi.org/10.1609/aaai.v39i25.34852.

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Assignment problems are a classic combinatorial optimization problem in which a group of agents must be assigned to a group of tasks such that maximum utility is achieved while satisfying assignment constraints. Given the utility of each agent completing each task, polynomial-time algorithms exist to solve a single assignment problem in its simplest form. However, in many modern-day applications such as satellite constellations, power grids, and mobile robot scheduling, assignment problems unfold over time, with the utility for a given assignment depending heavily on the state of the system. We apply multi-agent reinforcement learning to this problem, learning the value of assignments by bootstrapping from the known polynomial-time greedy solver and then learning from further experience. We then choose assignments using a distributed optimal assignment mechanism rather than by selecting them directly. We demonstrate that this algorithm is theoretically justified and avoids pitfalls experienced by other RL algorithms in this setting. Finally, we show that our algorithm significantly outperforms other methods in the literature, even while scaling to realistic scenarios with hundreds of agents and tasks.

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Wu, Xiaohu, Yihao Liu, Xueyan Tang, Wentong Cai, Funing Bai, Gilbert Khonstantine, and Guopeng Zhao. "Multi-Agent Pickup and Delivery with Task Deadlines." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (July 21, 2021): 206–8. http://dx.doi.org/10.1609/socs.v12i1.18585.

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We study the multi-agent pickup and delivery problem with task deadlines, where a team of agents execute tasks with individual deadlines to maximize the number of tasks completed by their deadlines. We take an integrated approach that assigns and plans one task at a time taking into account the agent states resulting from all the previous task assignments and path planning. We define metrics to effectively determine which agent ought to execute a given task and which task is most worth assignment next. We leverage the bounding technique to greatly improve the computational efficiency.

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Weng, Jiali, and Xike Xie. "Optimizing Spatial Crowdsourcing: A Quality- Aware Task Assignment Approach for Mobile Communication." Journal of Electronic Research and Application 8, no. 3 (June 14, 2024): 104–11. http://dx.doi.org/10.26689/jera.v8i3.7214.

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The widespread use of advanced electronic devices has led to the emergence of spatial crowdsourcing, a method that taps into collective efforts to perform real-world tasks like environmental monitoring and traffic surveillance. Our research focuses on a specific type of spatial crowdsourcing that involves ongoing, collaborative efforts for continuous spatial data acquisition. However, due to limited budgets and workforce availability, the collected data often lacks completeness, posing a data deficiency problem. To address this, we propose a reciprocal framework to optimize task assignments by leveraging the mutual benefits of spatiotemporal subtask execution. We introduce an entropy-based quality metric to capture the combined effects of incomplete data acquisition and interpolation imprecision. Building on this, we explore a quality-aware task assignment method, corresponding to spatiotemporal assignment strategies. Since the assignment problem is NP-hard, we develop a polynomial-time algorithm with the guaranteed approximation ratio. Novel indexing and pruning techniques are proposed to further enhance performance. Extensive experiments conducted on datasets validate the effectiveness of our methods.

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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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Wang, Weining. "Unmasking Illegitimate Task Assignment: Exploring Its Impact on Employee Well-being and the Role of Information Management Systems in HR." Journal of Information Systems Engineering and Management 9, no. 3 (July 5, 2024): 26259. http://dx.doi.org/10.55267/iadt.07.14776.

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This research aims to explore the direct relationship between the illegitimate task assignment and employee well-being. The relationship between the variables illegitimate task assignment and HR information management systems was also explored in this research. Moreover, this research also aims to explore the mediating role of HR information management systems among the proposed direct relationship of this research. To achieve the aim of this research the data was collected from the 319 employees of 13 software companies in Hunan Province, China. This research findings reveal a significant impact of illegitimate task assignment on employee well-being, mirroring the stress, job discontent, and heightened burnout documented in previous studies. HR information systems were discovered to both mediate and moderate the relationship, mitigating the negative effects of illegitimate work assignments. When properly overseen, these systems provide employees with valuable tools to deal with work-related difficulties. This study utilizes the Job Demand-Resource (JD-R) model to enhance comprehension of organizational dynamics and provide practical guidance for firms seeking to promote employee well-being in the modern workplace.

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McGahan, William T., Hardy Ernst, and Laurel Evelyn Dyson. "Individual Learning Strategies and Choice in Student-Generated Multimedia." International Journal of Mobile and Blended Learning 8, no. 3 (July 2016): 1–18. http://dx.doi.org/10.4018/ijmbl.2016070101.

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There has been an increasing focus on student-generated multimedia assessment as a way of introducing the benefits of both visual literacy and peer-mediated learning into university courses. One such assessment was offered to first-year health science students but, contrary to expectations, led to poorer performance in their end-of-semester examinations. Following an analysis, the assignment was redesigned to offer students a choice of either a group-based animation task or an individual written task. Results showed improved performance on the assignment when students were offered a choice of assignments over when they were offered only the multimedia assignment. Student feedback indicated that students adopt deliberate individual learning strategies when offered choices in assessment. The study suggests that assumptions regarding the superiority of student-generated multimedia over more traditional assessments are not always correct, but that students' agency and individual preferences need to be recognized.

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Jyoti, Arora, and Sharma Surbhi. "Solving Task Assignment Problem Using Branch and Bound Method." Journal of Applied Mathematics and Statistical Analysis 4, no. 1 (April 10, 2023): 1–7. https://doi.org/10.5281/zenodo.7813430.

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<em>The main aim of this paper is to present an algorithm of Branch and Bound method for solving task assignment problem. The branch and bound approach is based on divide and conquer method in which large problem is divided into smaller subsets of solutions. Our algorithm calculates lower bounds on solutions to the Task Assignment problem, which is further divided into subsets. Experimental results show that this Branch and Bound method to solve Task Assignment problem shows better results than standard Hungarian Method. A Numerical example is taken to illustrate the solution procedure</em>

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29

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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Ho, Chien-Ju, and Jennifer Vaughan. "Online Task Assignment in Crowdsourcing Markets." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 45–51. http://dx.doi.org/10.1609/aaai.v26i1.8120.

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We explore the problem of assigning heterogeneous tasks to workers with different, unknown skill sets in crowdsourcing markets such as Amazon Mechanical Turk. We first formalize the online task assignment problem, in which a requester has a fixed set of tasks and a budget that specifies how many times he would like each task completed. Workers arrive one at a time (with the same worker potentially arriving multiple times), and must be assigned to a task upon arrival. The goal is to allocate workers to tasks in a way that maximizes the total benefit that the requester obtains from the completed work. Inspired by recent research on the online adwords problem, we present a two-phase exploration-exploitation assignment algorithm and prove that it is competitive with respect to the optimal offline algorithm which has access to the unknown skill levels of each worker. We empirically evaluate this algorithm using data collected on Mechanical Turk and show that it performs better than random assignment or greedy algorithms. To our knowledge, this is the first work to extend the online primal-dual technique used in the online adwords problem to a scenario with unknown parameters, and the first to offer an empirical validation of an online primal-dual algorithm.

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31

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Schoonen, Rob. "Het Gebruik Van Gestructureerde En Correctieopdrachten In Peilingsonderzoek Naar Schrijfvaardigheid." Taaltoetsen 31 (January 1, 1988): 91–102. http://dx.doi.org/10.1075/ttwia.31.10sch.

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The assessment of writing ability is problematic, insofar as it concerns reliable, valid and practical measurement. In this article the possibilities of reducing error in both the writer's production and the judge's scoring are discussed. Special attention is paid to the reduction of the writing process (as described by Hayes and Flower) by structuring the assignment. Reducing the writing process by eliminating subprocesses from the assignment results in a reduction of the number of possible sources of error in the writer's production. However, the validity of the structured writing assignments might be questioned. An example of a structured assignment and a revision task are given, and some preliminary results are discussed.

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