Abstract
In this paper we investigate the use of three evolutionary based heuristics to the open shop scheduling problem. The intractability of this problem is a motivation for the pursuit of heuristics that produce approximate solutions. This work introduces three evolutionary based heuristics, namely, a permutation genetic algorithm, a hybrid genetic al- gorithm and a selfish gene algorithm, and tests their applicability to the open shop scheduling problem. Several problem instances are used with our evolutionary based algorithms. We compare the results and conclude with some observations and suggestions on the use of evolutionary heuristics for scheduling problems. We also report on the success that our hybrid genetic algorithm has had on one of the large benchmark problem instances: our heuristic has produced a better solution than the current best known solution.
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Khuri, S., Miryala, S.R. (1999). Genetic Algorithms for Solving Open Shop Scheduling Problems. In: Barahona, P., Alferes, J.J. (eds) Progress in Artificial Intelligence. EPIA 1999. Lecture Notes in Computer Science(), vol 1695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48159-1_25
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DOI: https://doi.org/10.1007/3-540-48159-1_25
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