Hybrid Pareto-Based Particle Swarm Optimization for Multi-Objective Flexible Job Shop Scheduling Problem

刘丽琴; 张学良; 谢黎明; 温淑花

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Hybrid Pareto-Based Particle Swarm Optimization for Multi-Objective Flexible Job Shop Scheduling Problem

更新时间：2026-09-14

- Hybrid Pareto-Based Particle Swarm Optimization for Multi-Objective Flexible Job Shop Scheduling Problem
- 多目标柔性作业车间调度的Pareto混合粒子群算法
- Vol. 34, Issue 2, Pages: 134-139(2013)
- 作者机构：
  
  1. 兰州理工大学机电工程学院
  2. 太原科技大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2013
- 稿件说明：
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刘丽琴, 张学良, 谢黎明, et al. Hybrid Pareto-Based Particle Swarm Optimization for Multi-Objective Flexible Job Shop Scheduling Problem[J]. 2013, 34(2): 134-139.
DOI：

刘丽琴, 张学良, 谢黎明, et al. Hybrid Pareto-Based Particle Swarm Optimization for Multi-Objective Flexible Job Shop Scheduling Problem[J]. 2013, 34(2): 134-139. DOI：

摘要

针对多目标柔性作业车间调度问题(Flexible Job-shop Scheduling Problem

FJSP)的复杂性

建立了以总完工时间、所有机器总负载及最大机器负载为目标函数的多目标调度模型

提出了一种带有Pareto档案集的混合粒子群优化算法.该算法首先通过全局搜索、局部搜索和随机产生相结合的初始化方法产生高质量的初始种群

利用改进的快速排序法构造Pareto档案集

并给出了一种快速更新Pareto档案集的算法.既保持了种群的多样性

避免了算法陷入局部最优

又有效地提高了算法的收敛速度;算法中还引入逆转策略来进一步增加解的多样性.通过3个经典算例的实验仿真

验证了该算法的可行性和有效性.

Abstract

A hybrid particle swarm optimization algorithm with Pareto archives set was proposed for solving the multi-objective flexible job-shop scheduling problem(FJSP).The multi-objective FJSP optimization model was developed

including the total completion time

the total workload and the maximal workload.To improve the quality of the initial population and accelerate the speed of the algorithm’s convergence

a new initialization method was proposed

which combined with global search

local search and random generation.Applying the improved quick sorting to reduce the time for computation

and a speed-up Pareto set updating algorithm based on dynamic crowding distance was developed to ensure sufficient diversity amongst the solutions of the non-dominated fronts.A reversion tactics was introduced to enhance the diversity of the solutions.Three typical simulation results were provided to demonstrate the feasibility and effectiveness of the proposed method.

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references

张超勇.基于改进非支配排序遗传算法的多目标柔性作业车间调度[J].机械工程学报,2010(11):156.Zhang Chaoyong.Improved NSGA-Ⅱfor the multi-objective flexible job-shop scheduling problem[J].Journal of Mechanical Engineering,2010(11):156.(in Chinese)

Bruker P.Job-Shop scheduling with multi-purposemachines[J].Computing,1990,45(4):369-375.

Xia W J,Wu Z M.An effective hybrid optimizationapproach for multi-objective flexible job-shopscheduling problem[J].Computers&IndustrialEngineering,2005,48:409-425.

刘明周.基于混合粒子群算法的多目标柔性Job-Shop调度方法[J].农业机械学报,2008,39(5):122.Liu Mingzhou.Multi-objective and flexible job-shopproblems study based on hybrid particle swarmoptimization[J].Transactions of the Chinese Societyfor Agricultural Machinery,2008,39(5):122.(inChinese)

Zhang G H,Shao X Y.An effective hybrid particleswarm optimization algorithm for multi-objectiveflexible job-shop scheduling problem[J].Computers&Industrial Engineering,2009,56:1309-1318.

Xing L N.Multi-objective flexible job-shop schedule:Design and evaluation by simulation modeling[J].Applied Soft Computing,2009,9:362-376.

Li J Q,Pan Q K,Liang Y C.An effective hybrid tabusearch algorithm for multi-objective flexible job-shopscheduling problems[J].Computers&IndustrialEngineering,2010,59:647-662.

Kacem I,Hammadi S,Borne P.Pareto-optimalityapproach for flexible job-shop scheduling problems:hybridization of evolutionary algorithms and fuzzylogic[J].Mathematics and Computers inSimutlation,2002,60(3):245-276.

王云.基于多目标粒子群算法的柔性作业车间调度优化方法[J].农业机械学报,2011,42(2):190.Wang Yun.Optimization method of flexible job-shopscheduling based on multiobjective particle swarmoptimization algorithm[J].Transactions of theChinese Society for Agricultural Machinery,2011,42(2):190.(in Chinese)

Ghasem M,Mehdi M.A pareto approach to multi-objective flexible job-shop scheduling problem usingparticle swarm optimization and local search[J].Int.Production Economics,2011,129:14-22.

刘丽琴.基于动态聚集距离的多目标粒子群优化算法及其应用[J].农业机械学报,2010,41(3):189.Liu Liqin.Multi-objective particle swarm opti-mization algorithm based on dynamic crowdingdistance and its application[J].Transactions of theChinese Society for Agricultural Machinery,2010,41(3):189.(in Chinese)

张国辉.改进遗传算法求解柔性作业车间调度问题[J].机械工程学报,2009,45(7):145.Zhang Guohui.Improved genetic algorithm for theflexible job-shop scheduling problem[J].J.Mechanical Eng.,2009,45(7):145.(in Chinese)

Kacem I,Hammadi S,Borne P.Approach bylocalization and multi-objective evolutionaryoptimization for flexible job-shop schedulingproblems[J].IEEE Transactions on Systems,Manand Cybernetics,Part C,2002,32(1):408-419.

李俊青,潘全科,王玉亭.多目标柔性车间调度的Pareto混合禁忌搜索算法[J].计算机集成制造系统,2010(7):1419.Li Junqing,Pan Quanke,Wang Yuting.HybridPareto-based tabu search algorithm for solving themulti-objective flexible job shop scheduling problem[J].Computer Integrated Manufacturing Systems,2010(7):1419.(in Chinese)

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