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An effective hybrid genetic algorithm for the job shop scheduling problem

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Abstract

From the computational point of view, the job shop scheduling problem (JSP) is one of the most notoriously intractable NP-hard optimization problems. This paper applies an effective hybrid genetic algorithm for the JSP. We proposed three novel features for this algorithm to solve the JSP. Firstly, a new full active schedule (FAS) procedure based on the operation-based representation is presented to construct a schedule. After a schedule is obtained, a local search heuristic is applied to improve the solution. Secondly, a new crossover operator, called the precedence operation crossover (POX), is proposed for the operation-based representation, which can preserve the meaningful characteristics of the previous generation. Thirdly, in order to reduce the disruptive effects of genetic operators, the approach of an improved generation alteration model is introduced. The proposed approaches are tested on some standard instances and compared with other approaches. The superior results validate the effectiveness of the proposed algorithm.

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Authors and Affiliations

  1. School of Mechanical Science & Engineering, Huazhong University of Science & Technology, Wuhan, 430074, People’s Republic of China

    Chaoyong Zhang, Yunqing Rao & Peigen Li

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  1. Chaoyong Zhang
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  2. Yunqing Rao
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  3. Peigen Li
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Correspondence to Chaoyong Zhang.

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Zhang, C., Rao, Y. & Li, P. An effective hybrid genetic algorithm for the job shop scheduling problem. Int J Adv Manuf Technol 39, 965–974 (2008). https://doi.org/10.1007/s00170-007-1354-8

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  • DOI: https://doi.org/10.1007/s00170-007-1354-8

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