An effective genetic algorithm for job shop scheduling

W. Wang, P. Brunn

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


This paper presents an effective genetic algorithm (GA) for job shop sequencing and scheduling. A simple and universal gene encoding scheme for both single machine and multiple machine models and their corresponding genetic operators, selection, sequence-extracting crossover and neighbour-swap mutation are described in detail. A simple heuristic rule is adapted and embedded into the GA to avoid the production of unfeasible solutions. The results of computing experiments for a number of scheduling problems have demonstrated that the GA described in the paper is effective and efficient in terms of the quality of solution and the computing cost.
Original languageEnglish
Pages (from-to)293-300
Number of pages8
JournalProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Issue number4
Publication statusPublished - 1 Apr 2000

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