冷链低碳物流配送路径优化的细菌觅食—蚁群算法研究
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摘要
冷链物流的绿色发展已成为国家十三五发展的热点,在分析冷链物流配送环节各种成本基础上,以车载容量和时间窗为约束,构建综合总成本最小化的冷链低碳物流配送路径优化模型.将细菌觅食算法中的复制操作和趋向操作引入基本蚁群算法中,改善了算法的收敛效率和全局搜索能力,提出了细菌觅食一蚁群算法用于求解冷链低碳物流配送路径优化模型.通过实例仿真表明,在求解冷链低碳物流配送路径优化模型方面,细菌觅食—蚁群算法能够以更高的效率寻找到更低的综合总成本,验证了改进算法的合理性和有效性.
Green development of cold chain logistics is one of the focus issue at present. On the basis of analyzing the costs of cold chain logistics, with the constraint of vehicle load and time windows, cold chain low carton logistics distribution routing optimization model with the objective of minimizing total cost is constructed. The reproduction operation and the chemotaxis operation are introduced to ant colony optimization to improve the convergence efficiency and global search capability, bacteria foraging ant colony optimization algorithm is proposed to solve the cold chain low carton logistics distribution route optimization model.Through a case simulation and comparison results show that bacteria foraging ant colony optimization algorithm can get less cost efficiently in solving the cold chain low carton logistics distribution routing optimization model, verified the reasonableness and effectiveness of this improved algorithm.
Green development of cold chain logistics is one of the focus issue at present. On the basis of analyzing the costs of cold chain logistics, with the constraint of vehicle load and time windows, cold chain low carton logistics distribution routing optimization model with the objective of minimizing total cost is constructed. The reproduction operation and the chemotaxis operation are introduced to ant colony optimization to improve the convergence efficiency and global search capability, bacteria foraging ant colony optimization algorithm is proposed to solve the cold chain low carton logistics distribution route optimization model.Through a case simulation and comparison results show that bacteria foraging ant colony optimization algorithm can get less cost efficiently in solving the cold chain low carton logistics distribution routing optimization model, verified the reasonableness and effectiveness of this improved algorithm.
引文
[1]毋庆刚.我国冷链物流发展现状与对策研究[J].中国流通经济,2011,25(2):24-28.
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[9]Zohal M,Soleimani H.Developing an ant colony approach for green closed-loop supply chain network design:a case study in gold industry[J].Journal of Cleaner Production,2016,133:314-337.
[10]王敏,毛超,杨葱葱.基于遗传算法的冷链物流节点选址研究[J].科技与管理,2009,11(6):72-75.
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[12]韩璐.基于广义费用的冷链物流配送优化研究[D].大连:大连海事大学硕士学位论文,2011.
[13]王雷,李明,刘志虎.基于吸引场的蚁群算法在TSP中的应用[J].江苏大学学报自然科学版,2015,36(5):573-577.
[14]Liu Y,Passino K M.Biomimicry of social foraging bacteria for distributed optimization:models,principles,and emergent behaviors[J].Journal of Optimization Theory and Applications,2002,115(3):603-628.
[15]胡洁.细菌觅食优化算法的改进及应用研究[D].武汉:武汉理工大学博士学位论文,2012.
[16]王勇臻,陈燕,李桃迎.离散型细菌觅食算法求解TSP[J].计算机应用研究,2014,31(12):3642-3645.
[17]费腾,张立毅,陈雷.配送中心选址问题的BFO-AFSA算法研究[J].计算机工程与应用,2015,51(23):1-5.
[18]尤梦丽,雷秀娟.改进的细菌觅食算法求解TSP问题[J].广西大学学报自然科学版,2013,38(6):1436-1443.
[19]叶志伟,郑肇葆.蚁群算法中参数α、β、ρ设置的研究——以TSP问题为例[J].武汉大学学报(信息科学版),2004,29(7):597-601.
[20]张立毅,王迎,费腾,等.混沌扰动模拟退火蚁群算法低碳物流路径优化[J].计算机工程与应用,2017,53(1):63-68.
[2]钱光宇.考虑碳排放的生鲜农产品冷链配送路径优化研究[D].北京:北京交通大学硕士学位论文,2016.
[3]王文铭,刘晓亮.我国冷链物流能耗现状及对策研究[J].中国流通经济,2011,25(10):29-33.
[4]杨珺,卢巍.低碳政策下多容量等级选址与配送问题研究[J].中国管理科学,2014,22(5):51-60.
[5]Bozorgi A,Pazour J,Nazzal D.A new inventory model for cold items that considers costs and emissions[J].International Journal of Production Economics,2014,155(5):114-125.
[6]潘茜茜,干宏程.考虑碳排放的冷链物流配送路径优化研究[J].数学的实践与认识,2016,46(2):62-68.
[7]Zhang S,Lee C K M,Choy K L,et al.Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem[J].Transportation Research Part D Transport and Environment,2014,31(8):85-99.
[8]Kalayci C B,Kaya C.An ant colony system empowered variable neighborhood search algorithm for the vehicle routing problem with simultaneous pickup and delivery[J].Expert Systems with Applications,2016,66:163-175.
[9]Zohal M,Soleimani H.Developing an ant colony approach for green closed-loop supply chain network design:a case study in gold industry[J].Journal of Cleaner Production,2016,133:314-337.
[10]王敏,毛超,杨葱葱.基于遗传算法的冷链物流节点选址研究[J].科技与管理,2009,11(6):72-75.
[11]Dorigo M.Ant Algorithms solve difficult optimization problems[C]//European Conference on Advances in Artificial Life.2001,2159:11-22.
[12]韩璐.基于广义费用的冷链物流配送优化研究[D].大连:大连海事大学硕士学位论文,2011.
[13]王雷,李明,刘志虎.基于吸引场的蚁群算法在TSP中的应用[J].江苏大学学报自然科学版,2015,36(5):573-577.
[14]Liu Y,Passino K M.Biomimicry of social foraging bacteria for distributed optimization:models,principles,and emergent behaviors[J].Journal of Optimization Theory and Applications,2002,115(3):603-628.
[15]胡洁.细菌觅食优化算法的改进及应用研究[D].武汉:武汉理工大学博士学位论文,2012.
[16]王勇臻,陈燕,李桃迎.离散型细菌觅食算法求解TSP[J].计算机应用研究,2014,31(12):3642-3645.
[17]费腾,张立毅,陈雷.配送中心选址问题的BFO-AFSA算法研究[J].计算机工程与应用,2015,51(23):1-5.
[18]尤梦丽,雷秀娟.改进的细菌觅食算法求解TSP问题[J].广西大学学报自然科学版,2013,38(6):1436-1443.
[19]叶志伟,郑肇葆.蚁群算法中参数α、β、ρ设置的研究——以TSP问题为例[J].武汉大学学报(信息科学版),2004,29(7):597-601.
[20]张立毅,王迎,费腾,等.混沌扰动模拟退火蚁群算法低碳物流路径优化[J].计算机工程与应用,2017,53(1):63-68.