Abstract
Much recent research has investigated the use of genetic algorithms (GAs) in job-shop scheduling. Mostly, this has involved comparison or construction of ingenious reprsentations and operators in the context of finding a schedule which minimises makespan. Many more criteria exist with which to judge schedule quality, however. Often, makespan may be a low priority aspect of schedule quality, We describe a generally-applicable GA approach to job-shop problems and examine its performance on a range of benchmark problems, for each of a wide range of different schedule quality criteria. Performance is compared against a range of standard heuristic rules, and also against a stochastic hillclimbing (SH) method. We find that the GA does best overall across all kinds of schedule quality criterion.
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Fang, H.L., Corne, D., Ross, P. (1996). A genetic algorithm for job-shop problems with various schedule quality criteria. In: Fogarty, T.C. (eds) Evolutionary Computing. AISB EC 1996. Lecture Notes in Computer Science, vol 1143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032771
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DOI: https://doi.org/10.1007/BFb0032771
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