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  

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

作　　者：李作成[1,2] 钱斌[1,2] 胡蓉[1,2] 罗蓉娟 张桂莲[1,2] 

机构地区：[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

出　　处：《化工学报》2014年第3期981-992,共12页CIESC Journal

基　　金：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)~~

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。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 helo the algorithm achieve a reasonable balance between ~lobal exoloration and local exoloitation abilities. Computer simulation showed the effectiveness and robustness of the proposed GA-EDA

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 

分 类 号：TQ021.8[化学工程]