Computational Intelligence in Flow Shop and Job Shop Scheduling

For over fifty years now, the famous problem of flow shop and job shop scheduling has been receiving the attention of researchers in operations research, engineering, and computer science. Over the past several years, there has been a spurt of interest in computational intelligence heuristics and metaheuristics for solving this problem. This book seeks to present a study of the state of the art in this field and also directions for future research.

List of contents

Structural Property and Meta-heuristic for the Flow Shop Scheduling Problem.- Scheduling in Flowshops with No-Idle Machines.- A Multi-objective Ant-colony Algorithm For Permutation Flowshop Scheduling To Minimize The Makespan And Total Flowtime Of Jobs.- Multi-Objective Simulated Annealing for Permutation Flow Shop Problems.- An estimation of distribution algorithm for minimizing the makespan in blocking flowshop scheduling problems.- A Scatter Search Method for Multiobjective Fuzzy Permutation Flow Shop Scheduling Problem: A Real World Application.- Genetic algorithm for job shop scheduling under uncertainty.- Giffler and Thompson Procedure based Genetic Algorithms for Scheduling Job Shops.- Scheduling Practice and Recent Developments in Flow Shop and Job Shop Scheduling.- Metaheuristics for Common Due Date Total Earliness and Tardiness Single Machine Scheduling Problem.

Product details

• Assisted by: Uday K. Chakraborty (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Released: 16.09.2009
• EAN: 9783642028359
• ISBN: 978-3-642-02835-9
• No. of pages: 345
• Dimensions: 157 mm x 239 mm x 25 mm
• Weight: 700 g
• Illustrations: VIII, 345 p.
• Series: Studies in Computational Intelligence
• Subjects: Natural sciences, medicine, IT, technology > Technology > General, dictionaries; Operations Research, C, Künstliche Intelligenz, Artificial Intelligence, Mutation, engineering, intelligence, Heuristics, Fuzzy, Mathematical and Computational Engineering, Engineering mathematics, Applied mathematics, Mathematical and Computational Engineering Applications, Uncertainty, genetic algorithms, metaheuristic