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A New MIP Model for Parallel-Batch Scheduling with Non-identical Job Sizes

  • Sebastian Kosch
  • J. Christopher Beck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8451)

Abstract

Parallel-batch machine problems arise in numerous manufacturing settings from semiconductor manufacturing to printing. They have recently been addressed in constraint programming (CP) via the combination of the novel sequenceEDD global constraint with the existing pack constraint to form the current state-of-the-art approach. In this paper, we present a detailed analysis of the problem and derivation of a number of properties that are exploited in a novel mixed integer programming (MIP) model for the problem. Our empirical results demonstrate that the new model is able to outperform the CP model across a range of standard benchmark problems. Further investigation shows that the new MIP formulation improves on the existing formulation primarily by producing a much smaller model and enabling high quality primal solutions to be found very quickly.

Keywords

Mixed Integer Programming Constraint Programming Master Problem Global Constraint Mixed Integer Programming Model 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Sebastian Kosch
    • 1
  • J. Christopher Beck
    • 1
  1. 1.Department of Mechanical & Industrial EngineeringUniversity of TorontoTorontoCanada

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