Heuristic scheduling of jobs on parallel batch machines with incompatible job families and unequal ready times

Lars Mönch, Hari Balasubramanian, John Fowler, Michele E. Pfund

Research output: Contribution to journalArticlepeer-review

156 Scopus citations

Abstract

This research is motivated by a scheduling problem found in the diffusion and oxidation areas of semiconductor wafer fabrication, where the machines can be modeled as parallel batch processors. We attempt to minimize total weighted tardiness on parallel batch machines with incompatible job families and unequal ready times of the jobs. Given that the problem is NP-hard, we propose two different decomposition approaches. The first approach forms fixed batches, then assigns these batches to the machines using a genetic algorithm (GA), and finally sequences the batches on individual machines. The second approach first assigns jobs to machines using a GA, then forms batches on each machine for the jobs assigned to it, and finally sequences these batches. Dispatching and scheduling rules are used for the batching phase and the sequencing phase of the two approaches. In addition, as part of the second decomposition approach, we develop variations of a time window heuristic based on a decision theory approach for forming and sequencing the batches on a single machine.

Original languageEnglish (US)
Pages (from-to)2731-2750
Number of pages20
JournalComputers and Operations Research
Volume32
Issue number11
DOIs
StatePublished - Nov 1 2005

Keywords

  • Batching
  • Genetic algorithms
  • Parallel machines
  • Scheduling
  • Semiconductor manufacturing

ASJC Scopus subject areas

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research

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