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Solution Representation for Job Shop Scheduling Problems in Ant Colony Optimisation

  • James Montgomery
  • Carole Fayad
  • Sanja Petrovic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)

Abstract

Production scheduling problems such as the job shop consist of a collection of operations (grouped into jobs) that must be scheduled for processing on different machines. Typical ant colony optimisation applications for these problems generate solutions by constructing a permutation of the operations, from which a deterministic algorithm can generate the actual schedule. This paper considers an alternative approach in which each machine is assigned a dispatching rule, which heuristically determines the order of operations on that machine. This representation creates a substantially smaller search space that likely contains good solutions. The performance of both approaches is compared on a real-world job shop scheduling problem in which processing times and job due dates are modelled with fuzzy sets. Results indicate that the new approach produces better solutions more quickly than the traditional approach.

Keywords

Solution Representation Production Schedule Problem Fuzzy Processing Time Satisfaction Grade Average Tardiness 
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 2006

Authors and Affiliations

  • James Montgomery
    • 1
  • Carole Fayad
    • 2
  • Sanja Petrovic
    • 2
  1. 1.Faculty of Information & Communication TechnologiesSwinburne University of TechnologyMelbourneAustralia
  2. 2.School of Computer Science & ITUniversity of NottinghamNottinghamUnited Kingdom

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