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Lateness minimization with Tabu search for job shop scheduling problem with sequence dependent setup times

Autor(es) y otros:
González Fernández, Miguel ÁngelAutoridad Uniovi; Rodríguez Vela, María del CaminoAutoridad Uniovi; González Rodríguez, InésAutoridad Uniovi; Varela Arias, José RamiroAutoridad Uniovi
Fecha de publicación:
2013
Editorial:

Springer

Versión del editor:
http://dx.doi.org/10.1007/s10845-011-0622-5
Citación:
Journal of Intelligent Manufacturing, 24(4), p. 741-754 (2013); doi:10.1007/s10845-011-0622-5
Descripción física:
p. 741-754
Resumen:

We tackle the job shop scheduling problem with sequence dependent setup times and maximum lateness minimization by means of a tabu search algorithm. We start by defining a disjunctive model for this problem, which allows us to study some properties of the problem. Using these properties we define a new local search neighborhood structure, which is then incorporated into the proposed tabu search algorithm. To assess the performance of this algorithm, we present the results of an extensive experimental study, including an analysis of the tabu search algorithm under different running conditions and a comparison with the state-of-the-art algorithms. The experiments are performed across two sets of conventional benchmarks with 960 and 17 instances respectively. The results demonstrate that the proposed tabu search algorithm is superior to the state-of-the-art methods both in quality and stability. In particular, our algorithm establishes new best solutions for 817 of the 960 instances of the first set and reaches the best known solutions in 16 of the 17 instances of the second set

We tackle the job shop scheduling problem with sequence dependent setup times and maximum lateness minimization by means of a tabu search algorithm. We start by defining a disjunctive model for this problem, which allows us to study some properties of the problem. Using these properties we define a new local search neighborhood structure, which is then incorporated into the proposed tabu search algorithm. To assess the performance of this algorithm, we present the results of an extensive experimental study, including an analysis of the tabu search algorithm under different running conditions and a comparison with the state-of-the-art algorithms. The experiments are performed across two sets of conventional benchmarks with 960 and 17 instances respectively. The results demonstrate that the proposed tabu search algorithm is superior to the state-of-the-art methods both in quality and stability. In particular, our algorithm establishes new best solutions for 817 of the 960 instances of the first set and reaches the best known solutions in 16 of the 17 instances of the second set

URI:
http://hdl.handle.net/10651/8055
ISSN:
0956-5515
Identificador local:

20120189

DOI:
10.1007/s10845-011-0622-5
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