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Satisfying flexible due dates in fuzzy job shop by means of hybrid evolutionary algorithms

Abstract: This paper tackles the job shop scheduling problem with fuzzy sets modelling uncertain durations and flexible due dates. The objective is to achieve high-service level by maximising due-date satisfaction, considering two different overall satisfaction measures as objective functions. We show how these functions model different attitudes in the framework of fuzzy multicriteria decision making and we define a measure of solution robustness based on an existing a-posteriori semantics of fuzzy schedules to further assess the quality of the obtained solutions. As solving method, we improve a memetic algorithm from the literature by incorporating a new heuristic mechanism to guide the search through plateaus of the fitness landscape. We assess the performance of the resulting algorithm with an extensive experimental study, including a parametric analysis, and a study of the algorithm?s components and synergy between them. We provide results on a set of existing and new benchmark instances for fuzzy job shop with flexible due dates that show the competitiveness of our method.

 Authorship: Palacios J.J., González-Rodríguez I., Vela C.R., Puente J.,

 Fuente: Integrated Computer-Aided Engineering, vol. 26, no. 1, pp. 65-84, 2019

 Publisher: IOS Press

 Year of publication: 2019

 No. of pages: 19

 Publication type: Article

 DOI: 10.3233/ICA-180583

 ISSN: 1069-2509,1875-8835

 Spanish project: TIN2016-79190-R

Authorship

PALACIOS, JUAN JOSÉ

VELA, CAMINO

PUENTE, JORGE