基于改进人工蜂群算法的易变质产品配送问题研究
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摘要
针对物流工程中的易变质产品配送路径优化问题,提出一种基于概率矩阵模型及精英保留策略的改进人工蜂群算法.利用贪婪准则初始化种群,用以提高初始种群质量;提出一种概率矩阵模型,通过记录各客户配送信息并将配送信息转化为概率来选择每代的配送顺序,同时结合精英保留策略,用以加快算法的收敛速度;为增加解序列的多样性,将单点交叉与顺序交叉相结合的交叉方式引入人工蜂群算法中.通过对实例的仿真及算法比较,验证改进的人工蜂群算法具有良好的全局收敛能力及较好的求解效果.
Aiming at the problem of distribution path optimization of perishable products in logistics engineering, proposed an improved artificial bee colony algorithm based on probability matrix model and elitist reservation strategy. Using the greedy criterion to initialize the population, in order to improve the quality of the initial population; proposed a probability matrix model, by recording each customer delivery information and the distribution information into the probability to choose the distribution order of each generation, combined with the elitist strategy is used to accelerate the convergence speed of the algorithm; in order to increase the solution diversity sequence, cross mode single point crossover and combination order crossover into artificial bee colony algorithm. Through the simulation of the example and the comparison of the algorithm, it is proved that the improved artificial bee colony algorithm has good global convergence and better results.
Aiming at the problem of distribution path optimization of perishable products in logistics engineering, proposed an improved artificial bee colony algorithm based on probability matrix model and elitist reservation strategy. Using the greedy criterion to initialize the population, in order to improve the quality of the initial population; proposed a probability matrix model, by recording each customer delivery information and the distribution information into the probability to choose the distribution order of each generation, combined with the elitist strategy is used to accelerate the convergence speed of the algorithm; in order to increase the solution diversity sequence, cross mode single point crossover and combination order crossover into artificial bee colony algorithm. Through the simulation of the example and the comparison of the algorithm, it is proved that the improved artificial bee colony algorithm has good global convergence and better results.
引文
[1]蒋国清,潘勇,胡飞跃.两阶段式的物流配送路径优化方法[J].计算机工程与应用,2015,51(2):255-258,264.
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[14]柳寅,马良.模糊人工蜂群算法的旅行商问题求解[J].计算机应用研究,2013,30(9):2694-2696.
[15]Zhang Ping-liang,Hu Kai-feng,Zhu Quan-xiang,et al.An enhanced artificial bee colony algorithm with adaptive differential operators[J].Applied Soft Computing,2017,58(5):480-494.
[16]Li Xi-xing,Peng Zhao,Du Bai-gang,et al.Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems[J].Computers&Industrial Engineering,2017,113(1):10-26.
[17]Gao Wei-feng,Huang Ling-ling,Liu San-yang,et al.Artificial bee colony algorithm with multiple search strategies[J].Applied Mathematics&Computation,2015,271(1):269-287.
[18]Nseef S K,Abdullah S,Turky A,et al.An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems[J].Knowledge-Based Systems,2016,104(1):14-23.
[19]胡中华,赵敏,撒鹏飞.基于人工蜂群算法的JSP的仿真与研究[J].机械科学与技术,2009,28(7):851-856.
[20]Chang Pei-chann,Chen Meng-hui.A block based estimation of distribution algorithm using bivariate model for scheduling problems[J].Soft Computing,2014,18(6):1177-1188.
[2]Kuo Yi-yo,Wang Chi-chang.A variable neighborhood search for the multi-depot vehicle routing problem with loading cost[J].Expert Systems with Application,2012,39(8):6949-6954.
[3]Bettinelli A,Righini G.A branch-and-price algorithm for the multi-depot heterogeneous vehicle routing problem with time windows[J].Transportation Research Part CEmerging Technologies,2011,19(5):723-740.
[4]Hu Jia,Shao Yun-li,Sun Zong-xuan,et al.Integrated vehicle and powertrain optimization for passenger vehicles with vehicle-infrastructure communication[J].Transportation Research Part C Emerging Technologies,2017,79(1):85-102.
[5]Subramanian A,Penna P H V,Uchoa E,et al.Ahybrid algorithm for the heterogeneous fleet vehicle routing problem[J].European Journal of Operational Research,2012,3(7):1021-1032.
[6]Repoussis P P,Tarantilis C D.Solving the fleet size and mix vehicle routing problem with time windows via adaptive memory programming[J].Transportation Research Part C Emerging Technologies,2010,18(5):695-712.
[7]Li Xiang-yong,Tian Peng.An adaptive memory programming meta heuristic for the heterogeneous fixed fleet vehicle routing problem[J].Transportation Research Part E-Logistics and Trasportation Review,2010,46(6):1111-1127.
[8]杨进,马良.蜂群算法在带时间窗的车辆路径问题中的应用[J].计算机应用研究,2009,26(11):4048-4050.
[9]郭晓梅,袁淑杰,王劲松,等.四川春玉米气象干旱致灾因子危险性[J].兰州大学学报:自然科学版,2017,53(1):79-87,92.
[10]冯颖,蔡小强,涂菶生.资金和库存空间约束下多种易变质产品的联合订购策略[J].兰州大学学报:自然科学版,2009,45(6):136-142.
[11]韩冰源,肖生苓.基于遗传算法的易腐货物即时配送路线的优化[J].东北林业大学学报,2007,35(2):70-72.
[12]庄景明,彭昕昀.基于改进遗传算法的新鲜农产品配送路线优化研究[J].江西师范大学学报:自然科学版,2012,36(4):399-402.
[13]蔡浩原,潘郁.基于人工蜂群算法的鲜活农产品冷链物流配送路径优化[J].江苏农业科学,2017,45(15):318-321.
[14]柳寅,马良.模糊人工蜂群算法的旅行商问题求解[J].计算机应用研究,2013,30(9):2694-2696.
[15]Zhang Ping-liang,Hu Kai-feng,Zhu Quan-xiang,et al.An enhanced artificial bee colony algorithm with adaptive differential operators[J].Applied Soft Computing,2017,58(5):480-494.
[16]Li Xi-xing,Peng Zhao,Du Bai-gang,et al.Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems[J].Computers&Industrial Engineering,2017,113(1):10-26.
[17]Gao Wei-feng,Huang Ling-ling,Liu San-yang,et al.Artificial bee colony algorithm with multiple search strategies[J].Applied Mathematics&Computation,2015,271(1):269-287.
[18]Nseef S K,Abdullah S,Turky A,et al.An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems[J].Knowledge-Based Systems,2016,104(1):14-23.
[19]胡中华,赵敏,撒鹏飞.基于人工蜂群算法的JSP的仿真与研究[J].机械科学与技术,2009,28(7):851-856.
[20]Chang Pei-chann,Chen Meng-hui.A block based estimation of distribution algorithm using bivariate model for scheduling problems[J].Soft Computing,2014,18(6):1177-1188.