Comparative Study of Meta-heuristics for Solving Flow Shop Scheduling Problem Under Fuzziness

Authors:
Noelia González;Camino R. Vela;Inés González-Rodríguez
Affiliations:
Centro de Inteligencia Artificial, Universidad de Oviedo, Campus de Viesques, E-33271, Gijón, Spain;Centro de Inteligencia Artificial, Universidad de Oviedo, Campus de Viesques, E-33271, Gijón, Spain;Centro de Inteligencia Artificial, Universidad de Oviedo, Campus de Viesques, E-33271, Gijón, Spain
Venue:
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Year:
2007

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Abstract

In this paper we propose a hybrid method, combining heuristics and local search, to solve flow shop scheduling problems under uncertainty. This method is compared with a genetic algorithm from the literature, enhanced with three new multi-objective functions. Both single objective and multi-objective approaches are taken for two optimisation goals: minimisation of completion time and fulfilment of due date constraints. We present results for newly generated examples that illustrate the effectiveness of each method.