Supervised Learning Linear Priority Dispatch Rules for Job-Shop Scheduling

  • Helga Ingimundardottir
  • Thomas Philip Runarsson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6683)

Abstract

This paper introduces a framework in which dispatching rules for job-shop scheduling problems are discovered by analysing the characteristics of optimal solutions. Training data is created via randomly generated job-shop problem instances and their corresponding optimal solution. Linear classification is applied in order to identify good choices from worse ones, at each dispatching time step, in a supervised learning fashion. The method is purely data-driven, thus less problem specific insights are needed from the human heuristic algorithm designer. Experimental studies show that the learned linear priority dispatching rules outperforms common single priority dispatching rules, with respect to minimum makespan.

Keywords

Problem Instance Optimal Schedule Schedule Rule Slack Time Partial Schedule 
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 2011

Authors and Affiliations

  • Helga Ingimundardottir
    • 1
  • Thomas Philip Runarsson
    • 1
  1. 1.School of Engineering and Natural SciencesUniversity of IcelandIceland

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