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Combining neighbourhoods in fuzzy job shop problems

Author:
Puente Peinador, JorgeUniovi authority; Rodríguez Vela, María del CaminoUniovi authority; González Rodríguez, InésUniovi authority
Publication date:
2011
Editorial:

Springer

Publisher version:
http://dx.doi.org/10.1007/978-3-642-25274-7_35
Citación:
Advances in Artificial Intelligence, p. 343-352 (2011); doi:10.1007/978-3-642-25274-7_35
Serie:

Lecture Notes in Computer Science;7023

Descripción física:
p. 343-352
Abstract:

In the sequel, we propose a new neighbourhood structure for local search for the fuzzy job shop scheduling problem, which is a variant of the well-known job shop problem, where uncertain durations are modelled as fuzzy numbers and the objective is to minimise the expected makespan of the resulting schedule. The new neighbourhood structure is based on changing the position of a task in a critical block. We provide feasibility conditions and a makespan estimate which allows to select only feasible and promising neighbours. The experimental results illustrate the success of our proposal in reducing expected makespan within a memetic algorithm. The experiments also show that combining the new structure with an existing neighbourhood from the literature considering both neighborhoods at the same time, provides the best results

In the sequel, we propose a new neighbourhood structure for local search for the fuzzy job shop scheduling problem, which is a variant of the well-known job shop problem, where uncertain durations are modelled as fuzzy numbers and the objective is to minimise the expected makespan of the resulting schedule. The new neighbourhood structure is based on changing the position of a task in a critical block. We provide feasibility conditions and a makespan estimate which allows to select only feasible and promising neighbours. The experimental results illustrate the success of our proposal in reducing expected makespan within a memetic algorithm. The experiments also show that combining the new structure with an existing neighbourhood from the literature considering both neighborhoods at the same time, provides the best results

Description:

Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011(14th. 2011. La Laguna, Spain)

URI:
http://hdl.handle.net/10651/7924
ISBN:
978-3-642-25273-0
ISSN:
0302-9743
Identificador local:

20111662

DOI:
10.1007/978-3-642-25274-7_35
Patrocinado por:

All authors are supported by MEC-FEDER Grant TIN2010-20976-C02-02

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