Multiagent Scheduling Models And Algorithms
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Multiagent Scheduling
Author: Alessandro Agnetis
language: en
Publisher: Springer Science & Business Media
Release Date: 2014-01-31
Scheduling theory has received a growing interest since its origins in the second half of the 20th century. Developed initially for the study of scheduling problems with a single objective, the theory has been recently extended to problems involving multiple criteria. However, this extension has still left a gap between the classical multi-criteria approaches and some real-life problems in which not all jobs contribute to the evaluation of each criterion. In this book, we close this gap by presenting and developing multi-agent scheduling models in which subsets of jobs sharing the same resources are evaluated by different criteria. Several scenarios are introduced, depending on the definition and the intersection structure of the job subsets. Complexity results, approximation schemes, heuristics and exact algorithms are discussed for single-machine and parallel-machine scheduling environments. Definitions and algorithms are illustrated with the help of examples and figures.
Due Date-Related Scheduling with Two Agents
This book provides an introduction to the models, methods, and results of some due date-related scheduling problems in the field of multiagent scheduling. In multiagent scheduling, two or more agents share a common processing resource and each agent wants to optimize its own objective function with respect to its own set of jobs. Since the agents have conflicting objective functions, they have to negotiate among themselves with regard to sharing the common resource to optimize their own objective functions. A key feature of due date-related scheduling concerns the way in which due dates are considered: they can be given parameters or decision variables. For the former case, the motivation stems from the need to improve inventory and production management. For the latter case, due date assignment becomes a challenging issue since the decision-maker has to balance inventory holding costs against the benefits of fulfifilling orders in time. As for due dates, this book addresses the following three different scenarios: (i) The due dates of the jobs from either one or both of the two agents are decision variables, which are determined using some due date assignment models; (ii) The due dates of jobs in each job set are considered as given parameters, whereas which due date corresponds to a given job needs to determine; and (iii) The due date of each job is exogenously given. When the last case is involved, the objective function of each agent is related to the number of just-in-time jobs that are completed exactly on their due dates. For each considered scenario, depending on the model settings, and on the objective function of each agent, this book addresses the complexity, and the design of efficient exact or approximated algorithms. This book aims at introducing the author's research achievements in due date-related scheduling with two agents. It is written for researchers and Ph.D. students working in scheduling theory and other members of scientific community who are interested in recent scheduling models. Our goal is to enable the reader to know about some new achievements on this topic.
Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability: The PAAMS Collection
This book constitutes the refereed proceedings of the workshops which complemented the 13th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2015, held in Salamanca, Spain, in June 2015. The 36 revised full papers presented were carefully reviewed and selected from 91 submissions. This volume presents the papers that have been accepted for the following workshops: Workshop on Agents and multi-agent Systems for AAL and e-HEALTH, Workshop on Agent-Based Solutions for Manufacturing and Supply Chain, Workshop on MAS for Complex Networks and Social Computation, Workshop on Intelligent Systems for Context-based Information Fusion, Workshop on Multi-agent based Applications for Smart Grids and Sustainable Energy Systems, Workshop on Multiagent System based Learning Environments, Workshop in Intelligent Human-Agent Societies.