Constraint Programming Approach for Scheduling Jobs with Release Times, Non-Identical Sizes, and Incompatible Families on Parallel Batching Machines

Andy Ham, John Fowler, Eray Cakici

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

We study a parallel batch-scheduling problem that involves the constraints of different job release times, non-identical job sizes, and incompatible job families, is addressed. Mixed integer programming and constraint programming (CP) models are proposed and tested on a set of common problem instances from a paper in the literature. Then, we compare the performance of the models with that of a variable neighborhood search (VNS) heuristic from the same paper. Computational results show that CP outperforms VNS with respect to solution quality and run time by 3.4%-6.8% and 47%-91%, respectively. When compared to optimal solutions, the results demonstrate CP is capable of generating a near optimal solution in a short amount of time.

Original languageEnglish (US)
Article number8010885
Pages (from-to)500-507
Number of pages8
JournalIEEE Transactions on Semiconductor Manufacturing
Volume30
Issue number4
DOIs
StatePublished - Nov 2017

Keywords

  • CP
  • MIP
  • Parallel batching
  • VNS
  • incompatible

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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