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Chance-constrained model for uncertain job shop scheduling problem

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Abstract

Job shop scheduling problem is well-investigated and widely applied in the fields of operational research, system engineering and automatic management. This paper employs uncertain programming to deal with the job shop scheduling problem with uncertain processing time and cost. First, a chance-constrained model is proposed under the framework of uncertainty theory. The model is equivalent to a crisp one by inverse uncertainty distribution method. Furthermore, three heuristic algorithms (genetic algorithm, particle swarm optimization, firefly algorithm) are employed for solving the model. Finally, a numerical example is solved by these three algorithms, and the results are analyzed to show which algorithm is better for solving the established model.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61273009).

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Authors and Affiliations

  1. Department of Applied Mathematics, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, People’s Republic of China

    Jiayu Shen & Yuanguo Zhu

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  1. Jiayu Shen
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  2. Yuanguo Zhu
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Correspondence to Yuanguo Zhu.

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Communicated by V. Loia.

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Shen, J., Zhu, Y. Chance-constrained model for uncertain job shop scheduling problem. Soft Comput 20, 2383–2391 (2016). https://doi.org/10.1007/s00500-015-1647-z

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