Evolutionary Scheduling

  • Keshav P. Dahal
  • Kay Chen Tan
  • Peter I. Cowling

Part of the Studies in Computational Intelligence book series (SCI, volume 49)

Table of contents

  1. Front Matter
    Pages I-XI
  2. Methodology

    1. Carlos Cotta, Antonio J. Fernàndez
      Pages 1-30
  3. Classical and Non-Classical Models of Production Scheduling

    1. Joc Cing Tay, Nhu Binh Ho
      Pages 101-124
    2. José Antonio Vàzquez Rodríguez, Abdellah Salhi
      Pages 125-142
    3. Leticia Cagnina, Susana Esquivel, Carlos A. Coello Coello
      Pages 143-164
    4. Kazi Shah Nawaz Ripon, Chi-Ho Tsang, Sam Kwong
      Pages 165-195
  4. Timetabling

    1. Dilip Datta, Kalyanmoy Deb, Carlos M. Fonseca
      Pages 197-236
    2. Rhydian Lewis, Ben Paechter, Olivia Rossi-Doria
      Pages 237-272
  5. Energy Applications

    1. Tapabrata Ray, Ruhul Sarker
      Pages 273-292
    2. Isamu Watanabe, Ikuo Kurihara, Yoshiki Nakachi
      Pages 293-311
    3. Keshav P. Dahal, Stuart J. Galloway
      Pages 349-382
  6. Networks

    1. Ju Hui Li, Meng Hiot Lim, Yew Soon Ong, Qi Cao
      Pages 383-403
    2. Chi Keong Goh, Wei Ling Lim, Yong Han Chew, Kay Chen Tan
      Pages 405-436
  7. Transport

    1. David Naso, Michele Surico, Biagio Turchiano
      Pages 465-483
    2. Stephen Baker, Axel Bender, Hussein Abbass, Ruhul Sarker
      Pages 485-511
  8. Business

    1. Ashutosh Tiwari, Kostas Vergidis, Rajkumar Roy
      Pages 513-541

About this book

Introduction

Evolutionary scheduling is a vital research domain at the interface of two important sciences - artificial intelligence and operational research. Scheduling problems are generally complex, large scale, constrained, and multi-objective in nature, and classical operational research techniques are often inadequate at solving them effectively. With the advent of computation intelligence, there is renewed interest in solving scheduling problems using evolutionary computational techniques. These techniques, which include genetic algorithms, genetic programming, evolutionary strategies, memetic algorithms, particle swarm optimization, ant colony systems, etc, are derived from biologically inspired concepts and are well-suited to solve scheduling problems since they are highly scalable and flexible in terms of handling constraints and multiple objectives. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling, and demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems. The intended readers of this book are engineers, researchers, practitioners, senior undergraduates, and graduate students who are interested in the field of evolutionary scheduling.

Keywords

Computational Intelligence Evolution Evolutionary Algorithms Fuzzy Operations Research algorithm artificial intelligence evolutionary algorithm evolutionary strategies genetic algorithms heuristics intelligence metaheuristic optimization programming

Editors and affiliations

  • Keshav P. Dahal
    • 1
  • Kay Chen Tan
    • 2
  • Peter I. Cowling
    • 3
  1. 1.Modeling Optimisation Scheduling and Intelligent Control (MOSAIC), Research Centre, Department of ComputingUniversity of BradfordBradfordUK
  2. 2.Department of Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore
  3. 3.Modeling Optimisation Scheduling and Intelligent Control (MOSAIC), Research Centre, Department of ComputingUniversity of BradfordBradfordUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-48584-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-48582-7
  • Online ISBN 978-3-540-48584-1
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
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