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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Improving Local Search for the Fuzzy Job Shop Using a Lower Bound
verfasst von : Jorge Puente, Camino R. Vela, Alejandro Hernández-Arauzo, Inés González-Rodríguez
Erschienen in: Current Topics in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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We consider the fuzzy job shop problem, where uncertain durations are modelled as fuzzy numbers and the objective is to minimise the expected makespan. A recent local search method from the literature has proved to be very competitive when used in combination with a genetic algorithm, but at the expense of a high computational cost. Our aim is to improve its efficiency with an alternative rescheduling algorithm and a makespan lower bound to prune non-improving neighbours. The experimental results illustrate the success of our proposals in reducing both CPU time and number of evaluated neighbours.