粒子群算法求解作业车间调度问题的研究
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摘要
良好的生产调度是企业实现科学管理和提高生产效率的前提。随着科技的发展,生产规模越来越大,复杂程度越来越高,市场竞争越来越强。合理地利用资源在有效时间内创造最大的价值,是现代制造工业所追求目标。作业车间调度是一个典型的调度模型,已经受到了许多国内外研究者的关注。因此,合理的利用优化算法实现调度问题的分配决策和时间决策,具有一定的理论价值和现实意义。粒子群优化算法是群体智能的典型代表,它具有原理简单,调节参数少,收敛速度快等优点,已经成功应用在工业,工程,经济等方面,成为优化算法的研究热点和前沿。
本文围绕粒子群算法及其应用,就如何改进标准粒子群算法性能及该算法在作业车间调度上的应用进行了深入研究。首先介绍了本文的研究背景和国内外的研究进展,给出了研究目的意义,近年来研究车间调度问题的方法。其次描述了作业车间调度问题,总结了离散粒子群算法的研究及改进方法。深入分析基本粒子群算法的原理,结合交叉变异,模拟退火思想,给出了适合作业车间调度问题的离散粒子群模型。然后,针对基本粒子群算法局部搜索性能差的缺点,改进了基于“第三参考点”的改进离散粒子群算法,仿真实验表明改进离散粒子群算法在静态作业车间调度问题上有更好的收敛性和有效性。并将改进离散算法应用到具有模糊加工时间和模糊交货期的作业车间调度问题上,实验结果表明,该算法在模糊作业车间调度上的可行性。最后改进了自适应离散粒子群算法,让粒子位置更新方程的控制参数随着进化代数和群体聚集度的变化调整,采用工序块变异方式作为局部搜索策略,更好地收敛到全局最优。
Good production scheduling is essential to effectiveness of enterprise management and efficiency of production. Production is more of large scale and complicated with the progress in technology, and market competition is becoming stronger. Modern manufacturing is aimed to create value as much as possible through rational utilization of resources within a given time period. Job shop scheduling problem, as a classic model of scheduling problem, remains a main concern for researchers home and abroad. Thus, to realize the decision-making process in job distribution and time through proper use of optimization algorithm is considered valuable in both theory and practice. Particle swarm optimization (PSO), a representative technology in collective intelligence, characteristic of simple principle, fewer parameters and rapid convergence, has been successfully applied in a variety of industries.And it is a hot term on the frontiers of the optimization algorithm research.
This paper is intended to present a deep study of how to improve the performance and applicability of standard particle swarm optimization. First it introduces the research background and progress in the job shop scheduling study. Second, by briefing the job-shop scheduling problem and outlining the current studies and improvements in particle swarm optimization, this study proposes a discrete particle swarm optimization strategy, in combination with genetic variation and simulated annealing. And then, in response to poor performance of local search, this study presents an improved discrete PSO algorithm on the basis of the third reference point. Simulation tests show that this algorithm performs better in convergence and efficiency. Also, this study has applied the improved algorithm to solve a fuzzy job shop problem with fuzzy processing time and fuzzy duedate, and has proved its feasibility through simulation tests.Finally, the study proposes adaptive discrete particle swarm optimization to converge the global extreme value, which in turn adjusts the control parameters in concert with the swarm's fitness and evolution.
本文围绕粒子群算法及其应用,就如何改进标准粒子群算法性能及该算法在作业车间调度上的应用进行了深入研究。首先介绍了本文的研究背景和国内外的研究进展,给出了研究目的意义,近年来研究车间调度问题的方法。其次描述了作业车间调度问题,总结了离散粒子群算法的研究及改进方法。深入分析基本粒子群算法的原理,结合交叉变异,模拟退火思想,给出了适合作业车间调度问题的离散粒子群模型。然后,针对基本粒子群算法局部搜索性能差的缺点,改进了基于“第三参考点”的改进离散粒子群算法,仿真实验表明改进离散粒子群算法在静态作业车间调度问题上有更好的收敛性和有效性。并将改进离散算法应用到具有模糊加工时间和模糊交货期的作业车间调度问题上,实验结果表明,该算法在模糊作业车间调度上的可行性。最后改进了自适应离散粒子群算法,让粒子位置更新方程的控制参数随着进化代数和群体聚集度的变化调整,采用工序块变异方式作为局部搜索策略,更好地收敛到全局最优。
Good production scheduling is essential to effectiveness of enterprise management and efficiency of production. Production is more of large scale and complicated with the progress in technology, and market competition is becoming stronger. Modern manufacturing is aimed to create value as much as possible through rational utilization of resources within a given time period. Job shop scheduling problem, as a classic model of scheduling problem, remains a main concern for researchers home and abroad. Thus, to realize the decision-making process in job distribution and time through proper use of optimization algorithm is considered valuable in both theory and practice. Particle swarm optimization (PSO), a representative technology in collective intelligence, characteristic of simple principle, fewer parameters and rapid convergence, has been successfully applied in a variety of industries.And it is a hot term on the frontiers of the optimization algorithm research.
This paper is intended to present a deep study of how to improve the performance and applicability of standard particle swarm optimization. First it introduces the research background and progress in the job shop scheduling study. Second, by briefing the job-shop scheduling problem and outlining the current studies and improvements in particle swarm optimization, this study proposes a discrete particle swarm optimization strategy, in combination with genetic variation and simulated annealing. And then, in response to poor performance of local search, this study presents an improved discrete PSO algorithm on the basis of the third reference point. Simulation tests show that this algorithm performs better in convergence and efficiency. Also, this study has applied the improved algorithm to solve a fuzzy job shop problem with fuzzy processing time and fuzzy duedate, and has proved its feasibility through simulation tests.Finally, the study proposes adaptive discrete particle swarm optimization to converge the global extreme value, which in turn adjusts the control parameters in concert with the swarm's fitness and evolution.
引文
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[3]徐新黎.生产调度问题的智能优化方法研究及应用[D].[博士学位论文].杭州：浙江工业大学,2008.
[4]Garey M,Johnson D,Sethi R.The complexity of flow shop and job shop scheduling[J].Mathematics of Operations Research,1976,1(2):117-129.
[5]Wang L.Intelligence optimization algorithms with applications [M].Beijing:Tsinghua University Press,2004.68-75.
[6]Roslof J,Harjunkoski 1,Bjorkqvist J,et al.An MILP-based reordering algorithm for complex industrial scheduling and rescheduling[J].Computers and Chemical Engineering.2001,25(4):821-828.
[7]Matta D,Guignard M.The performance of rolling production schedules in a process industry[J].HE Transactions,1995,27(5):564-573.
[8]冯剑,岳琪.模拟退火算法求解TSP问题[J].森林工程,2008,24(1)：94-96.
[9]Praveen K M,Purushothaman D,Krishnaswami S.Minimizing makespan in a flowshop with two batch-processing machines using simulated annealing[J]. Robotics and Computer-Integrated Manufacturing,,2009,25(3):667-679.
[10]Foo Y P S,Takefuji Y.Stochastic neural networks for solving job-shop scheduling(part I)problem representation[J].IEEE International Conference on Neural Network,1988: 275-282.
[11]Foo Y P S,Takefuji Y.Stochastic neural networks for solving job-shop scheduling(part II)architecture and simulations[J].IEEE International Conference on Neural Network, 1988:283-290.
[12]Van Hulle M M.goal programming network for mixed integer liner programming:a case study for the job-shop scheduling problem [J].Iternational Journal of Neural Networks, 1991,2(3):201-209.
[13]王万良,吴启迪,徐新黎.基于Hopfield神经网络的作业车间生产调度方法[J].自动化学报,2002,28(5)：838-844.
[14]Cheng R W,Gen M,Tsujimura Y.A tutorial survey of job-shop scheduling problems using genetic algorithms (I) representation[J].Comp Ind Eng,1996,30(4):983-997.
[15]王凌,郑大钟.基于遗传算法的Job Shop调度研究进展[J].控制与决策,2001,16(supp1)：641-646.
[16]Sakawa M,Kubota R.Two-objective fuzzy job shop scheduling through genetic algorithm[J].Electr and Comm Jpn Pt Ⅲ,2001,84(4):60-68.
[17]刘志雄.调度问题中的粒子群优化方法及其应用研究[D].[博士学位论文].武汉：.武 汉理工大学,2005.
[18]Ali H K, Behrooz K. A discrete particle swarm optimization algorithm for scheduling parallel machines[J].Computers & Industrial Engineering,2009,56(1):216-223.
[19]冯斌,石锦风,孙俊.基于QPSO算法的作业车间调度问题的研究[J].计算机工程与设计,2007,28(23)：5690-5693.
[20]Sha Y D,Hsu C Y.A hybrid particle swarm optimization for job shop scheduling problem [J].Computers and Industrial Engineering,2006,51(4):791-808.
[21]Lian Z G,Gu X S,Jiao B.A similar particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan [J].Applied Mathematics and Computation, 2006,175(1):773-785.
[22]Zhou C,Gao L,Gao H B.Particle swarm optimization based algorithm for permutation flow shop scheduling[J].Acta Electronica Sinica,2006,34(11):2008-2011.
[23]高超,蔡晓楠.基于亲和力的动态自适应粒子群算法[J].电脑知识与技术,2008,4(6)：1489-1491.
[24]刁东宇,赵英凯.带双重变异算子的自适应粒子群优化算法[J].计算机工程与设计,2009,30(5)：1186-1188.
[25]Colomi A,Dorigo M,Maniezzo V,et al.Ant system for job-shop scheduling [J].JORBLE,Belgian Journal of Operations Research,Statistics and Computer Science, 1994,34(1):39-53.
[26]孔凡国,黄伟.Job Shop调度问题自适应蚁群算法的研究[J].数字设计与数字制造,2006,5(2),40-42.
[27]王万良,赵澄,熊婧等.基于改进蚁群算法的柔性作业车间调度问题的求解方法[J].系统仿真学报,2008,20(16)：4326-4329.
[28]Timmis J,Knight T.Artificial immune system:Using the immune system as inspiration for data mining[M].In:Abbass H A,Sarker R A,Newton C S eds.Data Mining A Heuristic Approach.Hershey:Idea Publishing Group,2001.209-230.
[29]Chun J S,Jung H K,Hahn S Y.A study on comparison of optimization performance between immune algorithm and other heuristic algorithms[J].IEEE Transactions on Magnetics,1998,34(5):2972-2975.
[30]徐震浩,顾幸生.不确定条件下的flow shop问题的免疫调度算法[J].系统工程学报,2005,20(4)：374-380.
[31]牛刚刚,孙树栋,余建军等.免疫进化算法求解静态Job shop调度[J].机械工程学报,2006,42(5)：87-91.
[32]余建军,孙树栋,王军强等.免疫模拟退火算法及其在柔性动态Job Shop中的应用[J].中国机械工程,2007,18(7)：793-799.
[33]高亮,高海兵,周驰.基于粒子群优化的开放式车间调度[J].机械工程学报,2006,42(2)：129-134.
[34]Li L L,Wang L,Liu L H.An effective hybrid PSOSA strategy for optimization and its applicaion to parameter estimation[J].Applied Mathematics and Computation,2006,17(9): 135-146.
[35]王凌,郑大钟.一类GASA混合策略及其收敛性研究[J].控制与决策,1998,13(6)：669-672.
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