Optimal Task Assignments
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| Author | : Felipe Balmaceda |
| Publisher | : |
| Total Pages | : 53 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
This paper studies optimal task assignments in a setting where agents are expectation-based loss averse according to KoszegiRabin (2006) and KoszegiRabin (2007) and are compensated according to an aggregated performance measure in which tasks are technologically independent. We show that the optimal task assignment is determined by a trade-off between paying lower compensation costs and restricting the set of implementable effort profiles under multitasking. We show that loss aversion combined with how much the marginal cost of effort in one task increases with the effort chosen in other tasks determines when multitasking saves on compensation costs, but results in an implementation problem.
| Author | : Felipe Balmaceda |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
This paper studies optimal task assignments in a risk neutral principal-agent model in which agents are compensated according to an aggregated performance measure. The main trade-off involved is one in which specialization allows the implementation of any possible effort profile, while multitasking constraint the set of implementable effort profiles. Yet, the implementation of any effort profile in this set is less expensive than that under specialization. The principal prefers multitasking to specialization except when tasks are complements and the output after success is small enough so that it is not second-best optimal to implement high effort in each task. This result is robust to several extensions such as the existence of multiple performance measures.
| Author | : Anthony D. Nikias |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2003 |
| Genre | : |
| ISBN | : |
This study examines optimal task assignment and performance measurement under moral hazard in a two state setting where two agents are risk neutral and have limited liability. Both productive and incentive efficiency are affected by task assignment. The principal may assign to each agent either two heterogeneous, interrelated tasks (broad task assignment) or two homogeneous unrelated tasks (specialized task assignment). We provide conditions under which it is optimal to assign broad or specialized tasks and under which aggregate or disaggregate performance measurement is optimal. Under a broad task assignment approach, disaggregate performance measurement is sometimes preferred by the principal to aggregate performance measurement. Under a specialized task assignment approach, aggregate performance measurement is always optimal. Further, under conditions where disaggregate performance measurement is optimal under broad task assignment, the principal's welfare is greater if she uses specialized task assignment. Thus, if the principal can choose between broad or specialized task assignments, aggregate performance measures are preferred.
| Author | : Xinyi Zhong |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
We formalize and study the multi-goal task assignment and pathfinding (MG-TAPF) problem from both theoretical and algorithmic perspectives. The MG-TAPF problem is to compute an assignment of tasks to agents, where each task consists of a sequence of goal locations, and plan collision-free paths for agents that visit all goal locations of their assigned tasks in sequence. Theoretically, we prove that the MG-TAPF problem is NP-hard to solve optimally. We present algorithms that build upon algorithmic techniques for the multi-agent pathfinding problem and solve the MG-TAPF problem optimally and bounded-suboptimally. We experimentally compare these algorithms on a variety of different benchmark domains.
| Author | : Roy D. Nord |
| Publisher | : |
| Total Pages | : 44 |
| Release | : 1990 |
| Genre | : Decision making |
| ISBN | : |
| Author | : John S. Hughes |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
In this paper, we consider the role of production externalities in the task assignment problem. Milgrom and Roberts (1992) suggest that complementarities available when agents are assigned to diverse tasks are necessary to overcome distortions in effort allocations caused by an inability to fine tune incentives when agents' compensation is based on aggregate imperfect signals. Our analysis formalizes this intuition in a setting that encompasses externalities under both diverse and similar task assignments.
| Author | : Allison M. Douglas |
| Publisher | : |
| Total Pages | : 66 |
| Release | : 2007 |
| Genre | : |
| ISBN | : |
Assigning workers to tasks in an efficient and cost effective manner is a problem that nearly every company faces. This task assignment problem can be very time consuming to solve optimally. This difficulty increases as problem size increases. Most companies are large enough that it isn't feasible to find an optimal assignment; therefore a good heuristic method is needed. This project involved creating a new heuristic to solve this problem by combining the Greedy Algorithm with the Meta-RaPS method. The Greedy Algorithm is a near-sighted assignment procedure that chooses the best assignment at each step until a full solution is found. Although the Greedy Algorithm finds a good solution for small to medium sized problems, introducing randomness using the meta-heuristic Meta-RaPS results in a better solution. The new heuristic runs 5000 iterations and reports the best solution. The final Excel RTM VBA program solves a small sized problem in less than one minute, and is within 10% of the optimal solution, making it a good alternative to time consuming manual assignments. Although larger, more realistic problems will take longer to solve, good solutions will be available in a fraction of the time compared to solving them optimally.
| Author | : Panos M. Pardalos |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 317 |
| Release | : 2013-03-09 |
| Genre | : Computers |
| ISBN | : 1475731558 |
Nonlinear Assignment Problems (NAPs) are natural extensions of the classic Linear Assignment Problem, and despite the efforts of many researchers over the past three decades, they still remain some of the hardest combinatorial optimization problems to solve exactly. The purpose of this book is to provide in a single volume, major algorithmic aspects and applications of NAPs as contributed by leading international experts. The chapters included in this book are concerned with major applications and the latest algorithmic solution approaches for NAPs. Approximation algorithms, polyhedral methods, semidefinite programming approaches and heuristic procedures for NAPs are included, while applications of this problem class in the areas of multiple-target tracking in the context of military surveillance systems, of experimental high energy physics, and of parallel processing are presented. Audience: Researchers and graduate students in the areas of combinatorial optimization, mathematical programming, operations research, physics, and computer science.
| Author | : Hemant K. Bhargava |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 342 |
| Release | : 2002-12-31 |
| Genre | : Computers |
| ISBN | : 9781402072956 |
This book is a compilation of a selected subset of research articles presented at the Eighth INFORMS Computing Society Conference, held in Chandler, Arizona, from January 8 to 10, 2003. The articles in this book represent the diversity and depth of the interface between ORiMS (operations research and the management sciences) and CS/AI (computer science and artificial intelligence ). This volume starts with two papers that represent the reflective and integrative thinking that is critical to any scientific discipline. These two articles present philosophical perspectives on computation, covering a variety of traditional and newer methods for modeling, solving, and explaining mathematical models. The next set includes articles that study machine learning and computational heuristics, and is followed by articles that address issues in performance testing of solution algorithms and heuristics. These two sets of papers demonstrate the richness of thought that takes place at the ORiMS and CSI AI interface. The final set of articles demonstrates the usefulness of these and other methods at the interface towards solving problems in the real world, covering e-commerce, workflow, electronic negotiation, music, parallel computation, and telecommunications. The articles in this collection represent the results of cross-fertilization between ORiMS and CSI AI, making possible advances that could have not been achieved in isolation. The continuing aim ofthe INFORMS Computing Society and this research conference is to invigorate and further develop this interface.
| Author | : Chiara Di Francescomarino |
| Publisher | : Springer Nature |
| Total Pages | : 510 |
| Release | : 2023-08-31 |
| Genre | : Computers |
| ISBN | : 3031416201 |
This book constitutes the refereed proceedings of the 21st International Conference on Business Process Management, BPM 2023, which took place in Utrecht, The Netherlands, in September 2023. The 27 papers included in this book were carefully reviewed and selected from 151 submissions. They were organized in three main research tracks: Foundations, engineering, and management.
