期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 194, 期 3, 页码 650-662出版社
ELSEVIER
DOI: 10.1016/j.ejor.2007.12.035
关键词
Heuristics; Local search; Job shop problem
The job shop scheduling problem (JSSP) is a notoriously difficult problem in combinatorial optimization. Extensive investigation has been devoted to developing efficient algorithms to find optimal or near-optimal solutions. This paper proposes a new heuristic algorithm for the JSSP that effectively combines the classical shifting bottleneck procedure (SBP) with a dynamic and adaptive neighborhood search procedure. Our new search method, based on a filter-and-fan (F&F) procedure, uses the SBP as a subroutine to generate a starting solution and to enhance the best schedules produced. The F&F approach is a local search procedure that generates compound moves by a strategically abbreviated form of tree search. Computational results carried out on a standard set of 43 benchmark problems show that our F&F algorithm performs more robustly and effectively than a number of leading metaheuristic algorithms and rivals the best of these algorithms. (C) 2008 Elsevier B.V. All rights reserved.
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