Over the past three decades extensive search have been done on pure m-machine flow shop problems. Many researchers faced the Flow Shop Scheduling Problem (FSSP) by means of well-known heuristics which, are successfully used for certain instances of the problem providing a single acceptable solution. Current trends involve distinct evolutionary computation approaches. This work shows an implementation of diverse evolutionary approaches on a set of flow shop scheduling instances, including latest approaches using a multirecombination feature, Multiple Crossovers per Couple (MCPC), and partial replacement of the population when possible stagnation is detected. A discussion on implementation details, analysis and a comparison of evolutionary and conventional approaches to the problem are shown.
CITATION STYLE
Esquivel, S. C., Zuppa, F., & Gallard, R. H. (2000). Multirecombinated evolutionary algorithms for the flow shop scheduling problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1917, pp. 264–272). Springer Verlag. https://doi.org/10.1007/3-540-45356-3_26
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