Natural Computing

, Volume 18, Issue 4, pp 757–768 | Cite as

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

  • Yaping Fu
  • Hongfeng WangEmail author
  • Min Huang
  • Junwei Wang


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.


Multiobjective scheduling Flowshop scheduling Evolutionary multiobjective optimiation Genetic algorithm Decomposition Multipopulation 



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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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Yaping Fu
    • 1
    • 3
  • Hongfeng Wang
    • 1
    • 2
    Email author
  • Min Huang
    • 1
  • Junwei Wang
    • 2
  1. 1.College of Information Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.Department of Industrial and Manufacturing System EngineeringThe University of Hong KongHong KongHong Kong
  3. 3.Institute of Complexity ScienceQingdao UniversityQingdaoChina

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