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Evolutionary Scheduling

  • Book
  • © 2007

Overview

Editors:
  1. Keshav P. Dahal

    1. Modeling Optimisation Scheduling and Intelligent Control (MOSAIC), Research Centre, Department of Computing, University of Bradford, Bradford, UK

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  2. Kay Chen Tan

    1. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

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  3. Peter I. Cowling

    1. Modeling Optimisation Scheduling and Intelligent Control (MOSAIC), Research Centre, Department of Computing, University of Bradford, Bradford, UK

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  • Demonstrate the applicability of evolutionary computational techniques to solve scheduling problems

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

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Table of contents (22 chapters)

  1. Front Matter

    Pages I-XI
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  2. Methodology

    1. Memetic Algorithms in Planning, Scheduling, and Timetabling

      • Carlos Cotta, Antonio J. Fernàndez
      Pages 1-30

    2. Landscapes, Embedded Paths and Evolutionary Scheduling

      • Colin R. Reeves
      Pages 31-48

  3. Classical and Non-Classical Models of Production Scheduling

    1. Scheduling of Flow-Shop, Job-Shop, and Combined Scheduling Problems using MOEAs with Fixed and Variable Length Chromosomes

      • Mark P. Kleeman, Gary B. Lamont
      Pages 49-99

    2. Designing Dispatching Rules to Minimize Total Tardiness

      • Joc Cing Tay, Nhu Binh Ho
      Pages 101-124

    3. A Robust Meta-Hyper-Heuristic Approach to Hybrid Flow-Shop Scheduling

      • José Antonio Vàzquez Rodríguez, Abdellah Salhi
      Pages 125-142

    4. Hybrid Particle Swarm Optimizers in the Single Machine Scheduling Problem: An Experimental Study

      • Leticia Cagnina, Susana Esquivel, Carlos A. Coello Coello
      Pages 143-164

    5. An Evolutionary Approach for Solving the Multi-Objective Job-Shop Scheduling Problem

      • Kazi Shah Nawaz Ripon, Chi-Ho Tsang, Sam Kwong
      Pages 165-195

  4. Timetabling

    1. Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem

      • Dilip Datta, Kalyanmoy Deb, Carlos M. Fonseca
      Pages 197-236

    2. Metaheuristics for University Course Timetabling

      • Rhydian Lewis, Ben Paechter, Olivia Rossi-Doria
      Pages 237-272

  5. Energy Applications

    1. Optimum Oil Production Planning using an Evolutionary Approach

      • Tapabrata Ray, Ruhul Sarker
      Pages 273-292

    2. A Hybrid Evolutionary Algorithm for Service Restoration in Power Distribution Systems

      • Isamu Watanabe, Ikuo Kurihara, Yoshiki Nakachi
      Pages 293-311

    3. Particle Swarm Optimisation for Operational Planning: Unit Commitment and Economic Dispatch

      • P. Sriyanyong, Y. H. Song, P. J. Turner
      Pages 313-347

    4. Evolutionary Generator Maintenance Scheduling in Power Systems

      • Keshav P. Dahal, Stuart J. Galloway
      Pages 349-382

  6. Networks

    1. Evolvable Fuzzy Scheduling Scheme for Multiple-ChannelPacket Switching Network

      • Ju Hui Li, Meng Hiot Lim, Yew Soon Ong, Qi Cao
      Pages 383-403

    2. A Multi-Objective Evolutionary Algorithm for Channel Routing Problems

      • Chi Keong Goh, Wei Ling Lim, Yong Han Chew, Kay Chen Tan
      Pages 405-436

  7. Transport

    1. Simultaneous Planning and Scheduling for Multi-Autonomous Vehicles

      • D. K. Liu, A. K. Kulatunga
      Pages 437-464

    2. Scheduling Production and Distribution of Rapidly Perishable Materials with Hybrid GA's

      • David Naso, Michele Surico, Biagio Turchiano
      Pages 465-483

    3. A Scenario-based Evolutionary Scheduling Approach for Assessing Future Supply Chain Fleet Capabilities

      • Stephen Baker, Axel Bender, Hussein Abbass, Ruhul Sarker
      Pages 485-511

  8. Business

    1. Evolutionary Optimization of Business Process Designs

      • Ashutosh Tiwari, Kostas Vergidis, Rajkumar Roy
      Pages 513-541
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Keywords

About this book

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.

Editors and Affiliations

  • Modeling Optimisation Scheduling and Intelligent Control (MOSAIC), Research Centre, Department of Computing, University of Bradford, Bradford, UK

    Keshav P. Dahal, Peter I. Cowling

  • Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Kay Chen Tan

Bibliographic Information

  • Book Title: Evolutionary Scheduling

  • Editors: Keshav P. Dahal, Kay Chen Tan, Peter I. Cowling

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-540-48584-1

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2007

  • Hardcover ISBN: 978-3-540-48582-7Published: 15 February 2007

  • Softcover ISBN: 978-3-642-08017-3Published: 30 November 2010

  • eBook ISBN: 978-3-540-48584-1Published: 25 April 2007

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XI, 628

  • Topics: Artificial Intelligence, Mathematical and Computational Engineering

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