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Improving local search for the fuzzy job shop using a lower bound

Author:
Puente Peinador, JorgeUniovi authority; Rodríguez Vela, María del CaminoUniovi authority; Hernández Arauzo, AlejandroUniovi authority; González Rodríguez, InésUniovi authority
Publication date:
2010
Editorial:

Springer

Publisher version:
http://dx.doi.org/10.1007/978-3-642-14264-2_23
Citación:
Current Topics in Artificial Intelligence, p. 222-232 (2010); doi:10.1007/978-3-642-14264-2_23
Serie:

Lecture Notes in Computer Science ;5988

Descripción física:
p. 222-232
Abstract:

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

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

Description:

Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2009 (13th. 2009. Sevilla, España)

URI:
http://hdl.handle.net/10651/9473
ISSN:
0302-9743
Identificador local:

20101026

DOI:
10.1007/978-3-642-14264-2_23
Patrocinado por:

This work supported by MEC-FEDER Grants TIN2007- 67466-C02-01 and MTM2007-62799

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