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An uncertain parallel machine problem with deterioration and learning effect

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Abstract

An uncertain uniform parallel machine scheduling problem with job deterioration and a learning effect is considered. Job processing times, due dates, deterioration rates and learning rates are assumed to be uncertain variables. The objective functions are the total weight earliness, tardiness and makespan. Three mathematical programming models are presented, i.e., expected value model, pessimistic value model and measure chance model. These models can be converted into equivalent crisp models by the inverse uncertainty distribution method. A hybrid algorithm mixed with dispatching rules based on structural features is employed to solve the problem. Finally, computational experiments are presented to illustrate the effectiveness of proposed algorithms.

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Acknowledgements

I am grateful to the editor and the anonymous reviewers for their helpful suggestions on an earlier version of this paper. This work is supported by the National Natural Science Foundation of China (No. 61673011).

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Authors and Affiliations

  1. Department of Public Basic Courses, Nanjing Institute of Industry Technology, Nanjing, 210023, Jiangsu, People’s Republic of China

    Jiayu Shen

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  1. Jiayu Shen
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Correspondence to Jiayu Shen.

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Communicated by Hector Cancela.

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Shen, J. An uncertain parallel machine problem with deterioration and learning effect. Comp. Appl. Math. 38, 3 (2019). https://doi.org/10.1007/s40314-019-0789-5

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