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A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

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Abstract

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine (HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The (EDA) structure was used for global search while the teaching learning based optimization (TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

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Authors and Affiliations

  1. Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education (East China University of Science and Technology), Shanghai, 200237, China

    Ze-wen Sun  (孙泽文) & Xing-sheng Gu  (顾幸生)

Authors
  1. Ze-wen Sun  (孙泽文)
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  2. Xing-sheng Gu  (顾幸生)
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Corresponding author

Correspondence to Xing-sheng Gu  (顾幸生).

Additional information

Foundation item: Projects(61573144, 61773165, 61673175, 61174040) supported by the National Natural Science Foundation of China; Project(222201717006) supported by the Fundamental Research Funds for the Central Universities, China

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Sun, Zw., Gu, Xs. A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine. J. Cent. South Univ. 24, 1779–1788 (2017). https://doi.org/10.1007/s11771-017-3586-6

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  • DOI: https://doi.org/10.1007/s11771-017-3586-6

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