A genetic algorithm for solving the economic lot scheduling problem in flow shops

Jia Yen Huang*, Ming-Jong Yao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

In this study, we propose a hybrid genetic algorithm (HGA) to solve the economic lot scheduling problem in flow shops. The proposed HGA utilizes a so-called Proc PLM heuristic that tests feasibility for the candidate solutions obtained in the evolutionary process of genetic algorithm. When a candidate solution is infeasible, we propose to use a binary search heuristic to 'fix' the candidate solution so as to obtain a feasible solution with the minimal objective value. To evaluate the performance of the proposed HGA, we randomly generate a total of 2100 instances from seven levels of utilization rate ranged from 0.45 to 0.80. We solve each of those 2100 instances by the proposed HGA and the other solution approaches in the literature. Our experiments show that the proposed HGA outperforms traditional methods for solving the economic lot scheduling problem in flow shops.

Original languageEnglish
Pages (from-to)3737-3761
Number of pages25
JournalInternational Journal of Production Research
Volume46
Issue number14
DOIs
StatePublished - 1 Jul 2008

Keywords

  • Feasibility
  • Genetic algorithms
  • Heuristics
  • Lot scheduling

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