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Improved local search for job shop scheduling with uncertain durations

Author:
González Rodríguez, InésUniovi authority; Rodríguez Vela, María del CaminoUniovi authority; Puente Peinador, JorgeUniovi authority; Hernández Arauzo, AlejandroUniovi authority
Publication date:
2009
Editorial:

Association for the Advancement of Artificial Intelligence (AAAI)

Descripción física:
p. 154-161
Abstract:

This paper is concerned with local search methods to solve job shop scheduling problems with uncertain durations modelled as fuzzy numbers. Based on a neighbourhood structure from the literature, a reduced set of moves and the consequent structure are defined. Theoretical results show that the proposed neighbourhood contains all the improving solutions from the original neighbourhood and provide a sufficient condition for optimality. Additionally, a makespan lower bound is proposed which can be used to discard neighbours. Experimental results illustrate the good performance of both proposals, which considerably reduce the computational load of the local search, as well as a synergy effect when they are simultaneously used

This paper is concerned with local search methods to solve job shop scheduling problems with uncertain durations modelled as fuzzy numbers. Based on a neighbourhood structure from the literature, a reduced set of moves and the consequent structure are defined. Theoretical results show that the proposed neighbourhood contains all the improving solutions from the original neighbourhood and provide a sufficient condition for optimality. Additionally, a makespan lower bound is proposed which can be used to discard neighbours. Experimental results illustrate the good performance of both proposals, which considerably reduce the computational load of the local search, as well as a synergy effect when they are simultaneously used

URI:
http://hdl.handle.net/10651/31585
http://www.aaai.org/ocs/index.php/ICAPS/ICAPS09/paper/viewFile/738/1106
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