A genetic algorithm based heuristic for scheduling of virtual manufacturing cells (VMCs)

Saadettin Erhan Kesen, Sanchoy K. Das, Zülal Güngör

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

We present a genetic algorithm (GA) based heuristic approach for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. Scheduling objective is weighted makespan and total traveling distance minimization. The scheduling decisions are the (i) assignment of jobs to the machines, and (ii) the job start time at each machine. To evaluate the effectiveness of the GA heuristic we compare it with a mixed integer programming (MIP) solution. This is done on a wide range of benchmark problem. Computational results show that GA is promising in finding good solutions in very shorter times and can be substituted in the place of MIP model.

Original languageEnglish (US)
Pages (from-to)1148-1156
Number of pages9
JournalComputers and Operations Research
Volume37
Issue number6
DOIs
StatePublished - Jun 2010

All Science Journal Classification (ASJC) codes

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

Keywords

  • Flexible manufacturing systems (FMS)
  • Genetic algorithm (GA)
  • Mathematical model
  • Scheduling
  • Virtual manufacturing cells (VMCs)

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