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A Genetic Algorithm with Priority Rules for Solving Job-Shop Scheduling Problems

  • S. M. Kamrul Hasan
  • Ruhul Sarker
  • Daryl Essam
  • David Cornforth
Part of the Studies in Computational Intelligence book series (SCI, volume 250)

Abstract

The Job-Shop Scheduling Problem (JSSP) is one of the most difficult NP-hard combinatorial optimization problems. In this chapter, we consider JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. First, we develop a genetic algorithm (GA) based approach for solving JSSPs. We then introduce a number of priority rules to improve the performance of GA, such as partial re-ordering, gap reduction, and restricted swapping. The addition of these rules results in a new hybrid GA algorithm that is clearly superior to other well-known algorithms appearing in the literature. Results show that this new algorithm obtained optimal solutions for 27 out of 40 benchmark problems. It thus makes a significantly new contribution to the research into solving JSSPs.

Keywords

Genetic Algorithm Schedule Problem Local Search Greedy Randomize Adaptive Search Procedure Priority Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • S. M. Kamrul Hasan
    • 1
  • Ruhul Sarker
    • 1
  • Daryl Essam
    • 1
  • David Cornforth
    • 2
  1. 1.School of IT&EEUniversity of New South Wales at the Australian Defence Force AcademyCanberraAustralia
  2. 2.Division of Energy TechnologyCommonwealth Scientific and Industrial Research Organization, Murray Dwyer CircuitAustralia

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