Abstract
This paper describes a heuristic combination based genetic algorithm, (GA), for tackling dynamic job-shop scheduling problems. Our approach is novel in that the genome encodes a choice of algorithm to be used to produce a set of schedulable operations, alongside a choice of heuristic which is used to choose an operation from the resulting set. We test the approach on 12 instances of dynamic problems, using 4 different objectives to judge schedule quality. We find that our approach outperforms other heuristic combination methods, and also performs well compared to the most recently published results on a number of benchmark problems.
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© 1998 Springer-Verlag Berlin Heidelberg
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Hart, E., Ross, P. (1998). A heuristic combination method for solving job-shop scheduling problems. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056926
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DOI: https://doi.org/10.1007/BFb0056926
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