Zustand: Hervorragend | Sprache: Englisch | Produktart: B�cher | 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.

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Detalles bibliogr�ficos

T�tulo

Due Date-Related Scheduling with Two Agents : Models and Algorithms

Editor

Springer Nature Singapore

Fecha de publicaci�n

2020

Idioma

Ingl�s

ISBN 10

9811521042

ISBN 13

9789811521041

Encuadernaci�n

Encuadernaci�n de tapa dura

Estado

Hervorragend

Cat�logos de vendedores

B�cher

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Sinopsis

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.

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Yunqiang Yin received the Ph.D. degree in applied mathematics from Beijing Normal University, Beijing, China, in 2009. He is currently a Professor with the School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China. He has published over 80 papers in journals such as Naval Research Logistics, Omega, European Journal of Operational Research, IEEE Transactions on Systems, Man, and Cybernetics: Systems, International Journal of Production Research, Information Sciences, International Journal of Economics, Annals of Operations Research, Journal of Scheduling, and Computers & Operations Research. His current research interests include Operations Research and Optimization, and Logistics Management. He was named one of the “most cited scientists” in computer science by the Elsevier in 2014 to 2018, respectively.
Dujuan Wang received the B.S. and M.S. degrees in computer science andtechnology, and the Ph.D. degree in management science and engineering from the Dalian University of Technology (DUT), Dalian, China. She is currently an associate professor with the Business School, Sichuan University, Chengdu, China. He has published over 40 papers in journals such as Naval Research Logistics, Omega, European Journal of Operational Research, IEEE Transactions on Systems, Man, and Cybernetics: Systems, International Journal of Production Research, Information Sciences, International Journal of Economics, Annals of Operations Research, Journal of Scheduling, and Computers & Operations Research. Her current research interestsinclude scheduling theory, production and operations management, intelligent optimization, and computer applications. Dr. Wang is an Organizer of the IEEE Symposium on Computational Intelligence in E-Government 2016, and has been a member of the Program Committee of the 2015 International Conference on Advanced Computational Intelligence2015 and the Sixth International Conference on Swarm Intelligence 2015.
T.C.E Cheng received the B.Sc.Eng. (First Class Hons.) degree from the University of Hong Kong, Hong Kong, the M.Sc. degree from the University of Birmingham, Birmingham, U.K., and the Ph.D. and Sc.D. degrees in operation research from the University of Cambridge, Cambridge, U.K. He is currently the Dean of the Faculty of Business, a Fung Yiu King–Wing Hang Bank Professor of Business Administration, and the Chair Professor of Management with Hong Kong Polytechnic University, Hong Kong. He has previously taught in Canada, England, and Singapore. He has published over 600 papers in journals such as California Management Review, the Journal of Operations Management, Management Science, MIS Quarterly, Operations Research, Organization Science, and Production and Operations Management. He has coauthored 12 books published by Chapman and Hall, McGraw-Hill, and Springer. His currentresearch interests include operations management and operations research. Prof. Cheng was a recipient of the Outstanding Young Engineer of the Year Award from the Institute of Industrial Engineers, USA, in 1992, and the Croucher Award (the top research award in Hong Kong) in 2001. He was named one of the “most cited scientists” in all fields, in business and economics, in computer science, and in engineering from 2005 to 2015 by the ISI Web of Science in 2015. He is a Registered Professional Engineer and a seasoned Management Consultant. Over the years, he has secured over HK 50 million in research funding from both public and private sectors to support his diverse research programs.

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