Genetic algorithm-based subproblem solution procedures for a modified shifting bottleneck heuristic for complex job shops

Lars Mönch, Rene Schabacker, Detlef Pabst, John Fowler

Research output: Contribution to journalArticlepeer-review

83 Scopus citations

Abstract

In this paper, we consider a modified shifting bottleneck heuristic for complex job shops. The considered job shop environment contains parallel batching machines, machines with sequence-dependent setup times and reentrant process flows. Semiconductor wafer fabrication facilities (Wafer Fabs) are typical examples for manufacturing systems with these characteristics. Our primary performance measure is total weighted tardiness (TWT). The shifting bottleneck heuristic uses a disjunctive graph to decompose the overall scheduling into scheduling problems for single tool groups. The scheduling algorithms for these scheduling problems are called subproblem solution procedures (SSPs). In previous research, only subproblem solution procedures based on dispatching rules have been considered. In this paper, we are interested in how much we can gain in terms of TWT if we apply more sophisticated subproblem solution procedures like genetic algorithms for parallel machine scheduling. We conduct simulation experiments in a dynamic job shop environment in order to assess the performance of the suggested subproblem solution procedures. It turns out that using near to optimal subproblem solution procedures leads in many situations to improved results compared to dispatching-based subproblem solution procedures.

Original languageEnglish (US)
Pages (from-to)2100-2118
Number of pages19
JournalEuropean Journal of Operational Research
Volume177
Issue number3
DOIs
StatePublished - Mar 16 2007

Keywords

  • Complex job shops
  • Genetic algorithms
  • Scheduling
  • Shifting bottleneck heuristic

ASJC Scopus subject areas

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

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