An efficient knowledge-based algorithm for the flexible job shop scheduling problem

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摘要

Flexible job shop scheduling problem (FJSP) is quite a difficult combinatorial model. Various metaheuristic algorithms are used to find a local or global optimum solution for this problem. Among these algorithms, variable neighborhood search (VNS) is a capable one and makes use of a systematic change of neighborhood structure for evading local optimum. The search process for finding a local or global optimum solution by VNS is totally random. This is one of the weaknesses of this algorithm. To remedy this weakness of VNS, this paper combines VNS algorithm with a knowledge module and proposes knowledge-based VNS (KBVNS). In KBVNS, the VNS part searches the solution space to find good solutions and knowledge module extracts the knowledge of good solution and feed it back to the algorithm. This would make the search process more efficient. Computational results of the paper on different size test problems prove the efficiency of our algorithm for FJS problem.

论文关键词:Flexible job shop scheduling problem,Variable neighborhood search,Knowledge module,Neighborhood structures,Knowledge-based algorithm

论文评审过程:Received 3 July 2011, Revised 19 March 2012, Accepted 1 April 2012, Available online 27 June 2012.

论文官网地址:https://doi.org/10.1016/j.knosys.2012.04.001