Sequencing and Scheduling with Inaccurate Data

$295.00

Yuri N. Sotskov (Editor)
United Institute of Informatics Problems, Minsk, Belarus

Frank Werner (Editor)
Faculty of Mathematics, Otto-von-Guericke-University, Magdeburg, Germany

Series: Applied Statistical Science
BISAC: SCI003000

In many real-world applications, the problems with the data used for scheduling such as processing times, setup times, release dates or due dates is not exactly known before applying a specific solution algorithm which restricts practical aspects of scheduling theory. During the last decades, several approaches have been developed for sequencing and scheduling with inaccurate data, depending on whether the data is given as random numbers, fuzzy numbers or whether it is uncertain, i.e., it can take values from a given interval. This book considers the four major approaches for dealing with such problems: a stochastic approach, a fuzzy approach, a robust approach and a stability approach. Each of the four parts is devoted to one of these approaches.

First, it contains a survey chapter on this subject, as well as between further chapters, presenting some recent research results in the particular area. The book provides the reader with a comprehensive and up-to-date introduction into scheduling with inaccurate data. The four survey chapters deal with scheduling with stochastic approaches, fuzzy job-shop scheduling, minmax regret scheduling problems and a stability approach to sequencing and scheduling under uncertainty. This book will be useful for applied mathematicians, students and PhD students dealing with scheduling theory, optimization and calendar planning. (Imprint: Nova)

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Table of Contents

Table of Contents

Preface

Part I: Stochastic Approach

Chapter 1: Scheduling with Stochastic Approaches
(Xiaoqiang Cai, Xianyi Wu, Lianmin Zhang and Xian Zhou, Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong and others)

Chapter 2: Uncertainty in Stochastic Scheduling
(Tjark Vredeveld, Maastricht University, The Netherlands)

Chapter 3: Stochastic Scheduling with General Distributed Activity Durations Using Markov Activity Networks and Phase-Type Distributions
(Marcello Urgo, Mechanical Engineering Department, Politecnico di Milano, Via La Masa, Milano, Italy)

Part II: Fuzzy Approach

Chapter 4: Fuzzy Job-Shop Scheduling
(Masatoshi Sakawa, Faculty of Engineering, Hiroshima University, Higashi-Hiroshima, Japan)

Chapter 5: Fuzzy Multi-Project Rough-Cut Capacity Planning
(Malek Masmoudi, Erwin Hans, Roel Leus and Alain Hait, LASPI, F-42334, IUT de Roanne, University of Lyon, University of Saint-Etienne, Roanne, France and others)

Chapter 6: Fuzzy Resource Constraint Project Scheduling Problem
(Malek Masmoudi and Alain Hait, University of Lyon, University of Saint-Etienne, LASPI, F-42334, IUT de Roanne, Roanne, France and others)

Chapter 7: A Fuzzy Tailored Nonlinear Fluctuation Smoothing Rule for Job Dispatching in a Semiconductor Manufacturing Factory
(Toly Chen, Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan)

Part III: Robust Approach

Chapter 8: Minmax (Regret) Scheduling Problems
(Adam Kasperski and Pawel Zielinski, Institute of Industrial Engineering and Management, Wroclaw University of Technology, Wybrzeze Wyspianskiego, Wroclaw, Poland and others)

Chapter 9: Robust Assembly Line Balancing: State of the Art and New Research Perspectives
(Öncü Hazir and Alexandre Dolgui, TED Üniversitesi, Iktisadi ve Idari Bilimler Fakültesi, Ziya Gökalp Caddesi, Kolej, Cankaya, Ankara, Turkey and others)

Chapter 10: Dynamic Analysis of Supply Chain Robustness and Adaptation with the Help of Attainable Sets and Positional Optimization
(Dmitry Ivanov, Boris Sokolov, Inna Solovyeva and Semen Potryasaev, Berlin School of Economics and Law, Department of Business Administration. Berlin, Germany and others)

Chapter 11: A Bi-Criteria Approach to Scheduling in the Face of Uncertainty: Considering Robustness and Stability Simultaneously
(Selcuk Gören and Ihsan Sabuncuoglu, Department of Industrial Engineering, Abdullah Gül University, Kayseri, Turkey)

Part IV: Stability Approach

Chapter 12: A Stability Approach to Sequencing and Scheduling under Uncertainty
(Yuri N. Sotskov and Frank Werner, United Institute of Informatics Problems, Minsk, Belarus and others)

Chapter 13: Minimizing Total Flow Time under Uncertainty Using Optimality and Stability Boxes
(Yuri N. Sotskov and Natalia G. Egorova, United Institute of Informatics Problems, Minsk, Belarus)

Chapter 14: A Stability Approach to Two-Stage Scheduling Problems with Uncertain Processing Times
(Natalja M. Matsveichuk and Yuri N. Sotskov, United Institute of Informatics Problems, Minsk, Belarus)

Chapter 15: Accuracy and Stability Functions for a Problem of Minimization of a Linear Form on a Set of Substitutions
(Yury Nikulin, Department of Mathematics and Statistics, University of Turku, Turku, Finland)

Index

Reviews

“Often the problem data of a scheduling instance such as the processing times, the setup times, the release dates or the due dates are not exactly known before a particular solution algorithm is applied. This observation may substantially restrict practical aspects of scheduling theory. Within the last 40 years, several approaches have been developed for scheduling problems with inaccurate data depending on whether the data are given as random numbers, fuzzy numbers or whether they are uncertain (which means that they can take values from a given interval). This edited book considers the four major approaches for dealing with such problems: a stochastic approach, a fuzzy approach, a robust approach and a stability approach.” READ MORE…Dr. Erwin Pesch, Faculty of Economics and Business Administration, University in Siegen

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