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A decomposition based multiobjective genetic algorithm with adaptive multipopulation strategy for flowshop scheduling problem

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Abstract

Recently, the solution algorithm for multiobjective scheduling problems has gained more and more concerns from the community of operational research since many real-world scheduling problems usually involve multiple objectives. In this paper, a new evolutionary multiobjective optimization (EMO) algorithm, which is termed as decomposition based multiobjective genetic algorithm with adaptive multipopulation strategy (DMOGA-AMP), is proposed to addressmultiobjective permutation flowshop scheduling problems (PFSPs). In the proposed DMOGA-AMP algorithm, a subproblem decomposition scheme is employed to decompose a multiobjective PFSP into a number of scalar optimization subproblems and then introduce the decomposed subproblems into the running course of algorithm in an adaptive fashion, while a subpopulation construction method is employed to construct multiple subpopulations adaptively to optimize their corresponding subproblems in parallel. In addition, several special strategies on genetic operations, i.e., selection, crossover, mutation and elitism, are also designed to improve the performance of DMOGA-AMP for the investigated problem. Based on a set of test instances of multiobjective PFSP, experiments are carried out to investigate the performance of DMOGA-AMP in comparison with several state-of-the-art EMO algorithms. The experimental results show the better performance of the proposed DMOGA-AMP algorithm in multiobjective flowshop scheduling.

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Acknowledgements

This work was supported by the National Science Foundation for Distinguished Young Scholars of China (Grant Numbers 71325002, 61225012); the National Nature Science Foundation of China (Grant Numbers 71001018, 71071028, 71671032, 71571156); Fundamental Research Funds for the Central Universities (Grant Numbers N110204005, N130404017, N160402002); and Fundamental Research Funds for State Key Laboratory of Synthetical Automation for Process Industries (Grant Numbers 2013ZCX11, PAL-N201505), Shandong Provincial Natural Science Foundation of China (Grant Number ZR2016FP02), Qingdao Postdoctoral Research Project (Grant Number 2016027).

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  1. Yaping Fu

    Present address: Institute of Complexity Science, Qingdao University, Qingdao, China

Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, China

    Yaping Fu, Hongfeng Wang & Min Huang

  2. Department of Industrial and Manufacturing System Engineering, The University of Hong Kong, Pok Fu Lam Road, Hong Kong, Hong Kong

    Hongfeng Wang & Junwei Wang

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  1. Yaping Fu
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  2. Hongfeng Wang
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  3. Min Huang
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  4. Junwei Wang
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Correspondence to Hongfeng Wang.

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Fu, Y., Wang, H., Huang, M. et al. A decomposition based multiobjective genetic algorithm with adaptive multipopulation strategy for flowshop scheduling problem. Nat Comput 18, 757–768 (2019). https://doi.org/10.1007/s11047-016-9602-1

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