CIESC Journal ›› 2014, Vol. 65 ›› Issue (3): 981-992.DOI: 10.3969/j.issn.0438-1157.2014.03.031

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A genetic algorithm-estimation of distribution algorithm for a kind of heterogeneous parallel machine scheduling problem with multiple operations in chemical production

LI Zuocheng1,2, QIAN Bin1,2, HU Rong1,2, LUO Rongjuan3, ZHANG Guilian1,2   

  1. 1 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China;
    2 Key Laboratory of Computer Technologies Application of Yunnan Province, Kunming 650500, Yunnan, China;
    3 School of Economics, Yunnan University, Kunming 650091, Yunnan, China
  • Received:2013-07-05 Revised:2013-08-31 Online:2014-03-05 Published:2014-03-05
  • Supported by:

    supported by the National Natural Science Foundation of China (60904081), the Academic and Technical Leader Candidate Project for Young and Middle-Aged Persons of Yunnan Province (2012HB011) and Discipline Construction Team Project of Kunming University of Science and Technology (14078212).

遗传-分布估计算法求解化工生产中一类带多工序的异构并行机调度问题

李作成1,2, 钱斌1,2, 胡蓉1,2, 罗蓉娟3, 张桂莲1,2   

  1. 1 昆明理工大学信息工程与自动化学院, 云南 昆明 650500;
    2 云南省计算机技术应用重点实验室, 云南 昆明 650500;
    3 云南大学经济学院, 云南 昆明 650091
  • 通讯作者: 钱斌
  • 作者简介:李作成(1986—),男,硕士研究生。
  • 基金资助:

    国家自然科学基金项目(60904081);云南省中青年学术和技术带头人后备人才项目(2012HB011);昆明理工大学学科方向建设项目(14078212)。

Abstract: A genetic algorithm-estimation of distribution algorithm (GA-EDA) was proposed to optimize the makespan criterion for a kind of heterogeneous parallel machine scheduling problem, i.e., the heterogeneous parallel machine scheduling problem with multiple operations and sequence-dependent setup times (HPMSP_MOSST), which widely existed in chemical production. Firstly, a probability model training mechanism based on GA was presented and used to increase the information accumulation of the probability model at the initial stage of the evolution, and then the efficiency of search was improved. Secondly, an effective hybrid strategy of GA and EDA was designed to help the algorithm achieve a reasonable balance between global exploration and local exploitation abilities. Computer simulation showed the effectiveness and robustness of the proposed GA-EDA.

Key words: heterogeneous parallel machine, multiple operations, genetic algorithm, estimation of distribution algorithm, optimization, probability model, computer simulation

摘要: 针对化工生产中广泛存在的一类带多工序的异构并行机调度问题,即部分产品需多工序加工,同时不同产品间带序相关设置时间的异构并行机调度问题(heterogeneous parallel machine scheduling problem with multiple operations and sequence-dependent setup times, HPMSP_MOSST),提出了一种遗传-分布估计算法(genetic algorithm-estimation of distribution algorithm, GA-EDA),用于优化最早完工时间(makespan)。首先,提出了一种基于GA的概率模型训练机制,用来提高概率模型在算法进化初期的信息积累量,进而提高搜索的效率;其次,设计了一种有效的GA与EDA混合策略,使得算法的全局探索和局部开发能力得到合理平衡。计算机模拟验证了GA-EDA的有效性和鲁棒性。

关键词: 异构并行机, 多工序, 遗传算法, 分布估计算法, 优化, 概率模型, 计算机模拟

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