基于改进粒子群算法Ⅴ型非传统布局仓库通道优化设计
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摘要
Ⅴ型仓储布局是一种典型的非传统布局方式,针对Ⅴ型布局主通道设计的问题,将主通道抽象为若干个点连接而成的折线通道,每条拣货通道按物动量大小对仓库进行分区,采用更加符合实际的存取货物作业的概率不相等的非完全随机存储策略,建立最小化平均拣货距离的仓库主通道设计数学优化模型。其次,设计了基于极值扰动算子的改进粒子群优化算法(EDO-PSO)进行算法求解,利用极值扰动算子解决易陷入局部最优问题,采用并行深度搜索策略,提高算法性能,并用Benchmark函数与其他改进PSO算法对比验证算法性能。最后,结合具体实验数据仿真分析,计算结果表明,该方法在相同货位分配策略下,能有效缩短总拣货距离,验证了方法的有效性。
Flying-Ⅴ layout is a classic non-traditional warehouse layout,which operates more efficiently compared with traditional warehouse layout. Here a crossing aisles design approach to the flying-Ⅴ warehouse layout is studied. Firstly,the crossing aisles are abstracted into a conjoint curve,and non-complete random storage assignment strategy based on different probability in each picking aisle is considered,and then an optimization model for crossing aisle layout is built to minimize average picking distance. By adopting parallel search strategy,an improved particle swarm optimization algorithm with extremedisturbed operator( EDO-PSO) is developed. The specific encoding and operators are devised. Especially,in order to avoid local optimum,the periodic disturbing operator is given. Moreover,the algorithm performance is analyzed by comparing with other improved PSO algorithms with Benchmark functions. Finally,a case study is used to evaluate the effectiveness of the proposed algorithm. The calculation results demonstrate the proposed approach can effectively shorten the total picking distance in the same storage assignment strategy. Contributions of the paper are the modeling and algorithm to crossing aisle layout design in flying-Ⅴ warehouse.
Flying-Ⅴ layout is a classic non-traditional warehouse layout,which operates more efficiently compared with traditional warehouse layout. Here a crossing aisles design approach to the flying-Ⅴ warehouse layout is studied. Firstly,the crossing aisles are abstracted into a conjoint curve,and non-complete random storage assignment strategy based on different probability in each picking aisle is considered,and then an optimization model for crossing aisle layout is built to minimize average picking distance. By adopting parallel search strategy,an improved particle swarm optimization algorithm with extremedisturbed operator( EDO-PSO) is developed. The specific encoding and operators are devised. Especially,in order to avoid local optimum,the periodic disturbing operator is given. Moreover,the algorithm performance is analyzed by comparing with other improved PSO algorithms with Benchmark functions. Finally,a case study is used to evaluate the effectiveness of the proposed algorithm. The calculation results demonstrate the proposed approach can effectively shorten the total picking distance in the same storage assignment strategy. Contributions of the paper are the modeling and algorithm to crossing aisle layout design in flying-Ⅴ warehouse.
引文
[1] Gue K R,Meller R D. Aisle configurations for unit-load warehouse[J]. IIE Transactions,2009,41(3):171-182.
[2] Koster R D,Le-Duc T,Roodbergen K J. Design and control of warehouse order picking:a literature review[J]. European Journal of Operational Research,2007,182(2):481-501.
[3]蒋美仙,冯定忠,赵晏林等.基于改进Fishbone的物流仓库布局优化[J].系统工程理论与实践,2013,33(11):2920-2929.
[4] Rao S S,Aall G K. Class-based storage with exact sshaped traversal routeing in low-level picker-to-part systems[J]. International Journal of Production Research,2013,51(16):4979-4996.
[5] Koster R D,Poort E V D. Routing orderpickers in a warehouse:a comparison between optimal and heuristic solutions[J]. IIE Transactions,1998,30(5):469-480.
[6] Gue K R, Ivanovic G, Meller R D. A unit-load warehouse with multiple pickup and deposit points and non-traditional aisles[J]. Transportation Research Part E Logistics&Transportation Review, 2012, 48(4):795-806.
[7]宣登殿,杨新征.现代仓储系统货物入库分配优化模型及算法研究[J].公路交通科技,2014,31(12):153-158.
[8]郭进,江志斌.紧固件分拣中心拣选策略的动态EIQ分析[J].科学技术与工程,2012,12(11):2655-2659.
[9]田歆,汪寿阳,陈庆洪.仓储配送中ABC管理的优化问题及其实证[J].运筹与管理,2008,17(4):1-7.
[10]肖建,郑力.考虑需求相关性的多巷道仓库货位分配问题[J].计算机集成制造系统,2008,14(12):2447-2451.
[11] Kennedy J,Eberhart R C. Particle swarm optimization[C]. IEEE International Conference on Neural Networks,1995,4(8):1942-1948.
[12] Shi Y,Eberhart R. Modified particle swarm optimizer[C]//IEEE International Conference on Evolutionary Computation Proceedings,1998. IEEE World Congress on Computational Intelligence. IEEE Xplore, 1998:69-73.
[13]董文永,康岚兰,刘宇航,等.带自适应精英扰动及惯性权重的反向粒子群优化算法[J].通信学报,2016(12):1-10.
[14]敖永才,师奕兵,张伟,等.自适应惯性权重的改进粒子群算法[J].电子科技大学学报,2014(6):874-880.
[15]史娇娇,姜淑娟,韩寒,等.自适应粒子群优化算法及其在测试数据生成中的应用研究[J].电子学报,2013,41(8):1555-1559.
[16] Clerc M. The swarm and the queen:towards a deterministic and adaptive particle swarm optimization[].Washington:Proc of the ICEC,1999. 1951-1957.
[17]张选平,杜玉平,秦国强,等.一种动态改变惯性权的自适应粒子群算法[J].西安交通大学学报,2005,39(10):1039-1042.
[18]吴晓军,杨战中,赵明.均匀搜索粒子群算法[J].电子学报,2011,39(6):1261-1266.
[2] Koster R D,Le-Duc T,Roodbergen K J. Design and control of warehouse order picking:a literature review[J]. European Journal of Operational Research,2007,182(2):481-501.
[3]蒋美仙,冯定忠,赵晏林等.基于改进Fishbone的物流仓库布局优化[J].系统工程理论与实践,2013,33(11):2920-2929.
[4] Rao S S,Aall G K. Class-based storage with exact sshaped traversal routeing in low-level picker-to-part systems[J]. International Journal of Production Research,2013,51(16):4979-4996.
[5] Koster R D,Poort E V D. Routing orderpickers in a warehouse:a comparison between optimal and heuristic solutions[J]. IIE Transactions,1998,30(5):469-480.
[6] Gue K R, Ivanovic G, Meller R D. A unit-load warehouse with multiple pickup and deposit points and non-traditional aisles[J]. Transportation Research Part E Logistics&Transportation Review, 2012, 48(4):795-806.
[7]宣登殿,杨新征.现代仓储系统货物入库分配优化模型及算法研究[J].公路交通科技,2014,31(12):153-158.
[8]郭进,江志斌.紧固件分拣中心拣选策略的动态EIQ分析[J].科学技术与工程,2012,12(11):2655-2659.
[9]田歆,汪寿阳,陈庆洪.仓储配送中ABC管理的优化问题及其实证[J].运筹与管理,2008,17(4):1-7.
[10]肖建,郑力.考虑需求相关性的多巷道仓库货位分配问题[J].计算机集成制造系统,2008,14(12):2447-2451.
[11] Kennedy J,Eberhart R C. Particle swarm optimization[C]. IEEE International Conference on Neural Networks,1995,4(8):1942-1948.
[12] Shi Y,Eberhart R. Modified particle swarm optimizer[C]//IEEE International Conference on Evolutionary Computation Proceedings,1998. IEEE World Congress on Computational Intelligence. IEEE Xplore, 1998:69-73.
[13]董文永,康岚兰,刘宇航,等.带自适应精英扰动及惯性权重的反向粒子群优化算法[J].通信学报,2016(12):1-10.
[14]敖永才,师奕兵,张伟,等.自适应惯性权重的改进粒子群算法[J].电子科技大学学报,2014(6):874-880.
[15]史娇娇,姜淑娟,韩寒,等.自适应粒子群优化算法及其在测试数据生成中的应用研究[J].电子学报,2013,41(8):1555-1559.
[16] Clerc M. The swarm and the queen:towards a deterministic and adaptive particle swarm optimization[].Washington:Proc of the ICEC,1999. 1951-1957.
[17]张选平,杜玉平,秦国强,等.一种动态改变惯性权的自适应粒子群算法[J].西安交通大学学报,2005,39(10):1039-1042.
[18]吴晓军,杨战中,赵明.均匀搜索粒子群算法[J].电子学报,2011,39(6):1261-1266.