云自适应遗传算法有能力约束的车辆调度优化  

Research on capacitated vehicle routing problem with cloud adaptive genetic algorithm

作　　者：蹇洁[1,2] 王旭[1] 葛显龙[3] 

机构地区：[1]重庆大学机械工程学院,重庆400044 [2]重庆邮电大学经济管理学院,重庆400065 [3]重庆交通大学管理学院,重庆400074

出　　处：《重庆大学学报（自然科学版）》2013年第8期40-46,共7页Journal of Chongqing University

基　　金：重庆市决策咨询与管理创新计划资助项目(CSTC2013JCCXA0109);工业与信息化部软科学资助项目(2013-R-10-2);重庆邮电大学社会科学基金资助项目(K2012-95);国家社会科学基金资助项目(11BGL006)

摘　　要：针对与车辆调度成本密切相关的运输量和车辆利用率,建立油耗费用和固定费用最小的车辆调度模型。根据车辆调度问题实时性和复杂性的要求,提出云模型理论与遗传算法相结合的云自适应遗传算法,利用云模型云滴的随机性和稳定倾向性改进标准遗传算法中固定设置交叉和变异概率的方式,克服了标准遗传算法搜索速度慢及易早熟的缺陷,设计基于最大保留机制的交叉和变异算子,提高了算法的收敛性和鲁棒性。最后,结合算例对模型和算法的有效性进行验证。Aiming at traffic volume and vehicle utilization,which are closely related to the cost of vehicle traffic,a vehicle scheduling model with the minimum fuel cost and fixed cost is established. According to the requirement of real-time and complicacy of the vehicle scheduling,a cloud adaptive genetic algorithm is proposed by combining cloud model theory with genetic algorithm. The way of the fixed set crossover and mutation probability in the standard genetic algorithm is improved by using the randomness and bias stability of the cloud droplet cloud model. Defects of slow search and easy precocious of the standard genetic algorithm is overcome. The convergence and robustness of the algorithm was improved by crossover and mutation that was designed based on maximum retention mechanism. Finally,an example authenticated the effectiveness of the model and algorithm.

关 键 词：车辆调度问题 标准遗传算法 云遗传算法 云模型 

分 类 号：F274[经济管理—企业管理]