电子标签卷烟分拣系统的仿真与优化
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摘要
由于电子标签分拣系统成本较低，所以在全国各地的烟草配送中心都在大量使用。电子标签卷烟分拣系统由装有电子标签的多组货架组成，在分拣过程中订单特点、卷烟在货架上的摆放位置等因素，都会给整个系统的分拣效率带来很大的影响。因此，对电子标签卷烟分拣系统的控制策略进行研究就有迫切的实际需要。
本文针对电子标签卷烟分拣系统设计开发了仿真程序，用于模拟不同工作模式下的分拣状况。该仿真程序主要模拟了卷烟从分拣货架单元上被取下的动作和卷烟落到皮带上以后，随皮带一起前移的动作。通过仿真可以直观地看到不同分拣模式的卷烟分拣效果。该仿真程序还对仿真的数据结果进行了分析，并提供了卷烟分拣条数、分拣订单数、分拣时间以及分拣效率等重要信息。仿真数据选用烟草配送中心的实际订单数据，能够反映实际的工作情况。对不同的订单数据进行仿真，可以分析出订单特性对分拣系统性能的影响。在仿真程序中分拣系统的属性，如皮带速度、单次取烟时间、跨货架时间等参数都可以设置。通过不同分拣模式的比较，可以分析出分拣参数对分拣系统性能的影响。
本文综合考虑了订单特点、卷烟在货架上的摆放位置和三种不同分拣模式对电子标签卷烟分拣系统的影响。运用EIQ分析方法对订单进行了ABC分类。在不同分拣模式的前提下，对不同类型的卷烟在货架上的摆放位置进行调换，得出了不同分拣模式下的仿真结果。仿真结果表明：四订单分拣模式效率明显高于单订单分拣效率，双订单模式分拣效率居中，但是它们之间并非是完全线性关系，其受到订单大小和卷烟摆放位置等因素的影响。在同一分拣模式下，对订单数据进行ABC分类后，再根据平均分配工作量的原则，在货架上对卷烟进行合理布置的分拣效率要比不进行分类的分拣效率要高。在不考虑补烟的情况下，大订单的分拣效率要比小订单高很多。
Electronic Labeling Picking Systems have lower costs, so they have been widely used in cigarette distribution center. Electronic labeling cigarette picking Systems are composed by many single shelf with electronic labeling. During the picking process, different kinds of orders and the position which cigarette put in shelves etc have great effect in assorting efficiency of the whole electronic labeling systems. So, there is an urgent demand to investigate the control policy of electronic labeling picking system.
The paper has designed a simulation program, for simulate the picking condition of different picking models. The main actions of simulation are cigarettes fall on the strap from picking shelf, and cigarettes move forwards in the strap. Picking condition of different picking models can be seen intuitively through simulation program. This program still can offer picking information of simulation, such as the numbers of picking orders and picking cigarette, picking time, efficiency, etc. The data of order come from the real work data of cigarette distribution center, and take on quite representation. At the same time, we can also update the order data. Picking attribute such as speed of belt, the one time of taking cigarette from the shelves and the time of crossing over the shelves side by side, can be changed in simulation program. The influence to picking process of picking parameters and the character of orders, can be analyzed through the comparisons of different picking models.
In the paper, factors of effecting on electronic labeling system, such as the character of order, the position which cigarette put in shelves and three different kinds of picking models, are synthetically considered. Orders are classified by ABC classify of EIQ analysis method, In the different picking model, the result of simulation has been obtained through changing the place in which different kind of cigarettes are put. The result shows that, picking efficiency of the four-order model is great higher than that of the one-order model, and picking efficiency of the two-order model is middle between them. But they aren't absolutely linear relationships, and they have been affected by the character of orders and the position of cigarettes etc. In the same
picking models, after orders are classified by ABC, the efficiency of putting in the
right place is higher than that which is not classified by ABC. Not considering
supplying with cigarette, the efficiency of big orders is much higher than that of little
orders.
本文针对电子标签卷烟分拣系统设计开发了仿真程序，用于模拟不同工作模式下的分拣状况。该仿真程序主要模拟了卷烟从分拣货架单元上被取下的动作和卷烟落到皮带上以后，随皮带一起前移的动作。通过仿真可以直观地看到不同分拣模式的卷烟分拣效果。该仿真程序还对仿真的数据结果进行了分析，并提供了卷烟分拣条数、分拣订单数、分拣时间以及分拣效率等重要信息。仿真数据选用烟草配送中心的实际订单数据，能够反映实际的工作情况。对不同的订单数据进行仿真，可以分析出订单特性对分拣系统性能的影响。在仿真程序中分拣系统的属性，如皮带速度、单次取烟时间、跨货架时间等参数都可以设置。通过不同分拣模式的比较，可以分析出分拣参数对分拣系统性能的影响。
本文综合考虑了订单特点、卷烟在货架上的摆放位置和三种不同分拣模式对电子标签卷烟分拣系统的影响。运用EIQ分析方法对订单进行了ABC分类。在不同分拣模式的前提下，对不同类型的卷烟在货架上的摆放位置进行调换，得出了不同分拣模式下的仿真结果。仿真结果表明：四订单分拣模式效率明显高于单订单分拣效率，双订单模式分拣效率居中，但是它们之间并非是完全线性关系，其受到订单大小和卷烟摆放位置等因素的影响。在同一分拣模式下，对订单数据进行ABC分类后，再根据平均分配工作量的原则，在货架上对卷烟进行合理布置的分拣效率要比不进行分类的分拣效率要高。在不考虑补烟的情况下，大订单的分拣效率要比小订单高很多。
Electronic Labeling Picking Systems have lower costs, so they have been widely used in cigarette distribution center. Electronic labeling cigarette picking Systems are composed by many single shelf with electronic labeling. During the picking process, different kinds of orders and the position which cigarette put in shelves etc have great effect in assorting efficiency of the whole electronic labeling systems. So, there is an urgent demand to investigate the control policy of electronic labeling picking system.
The paper has designed a simulation program, for simulate the picking condition of different picking models. The main actions of simulation are cigarettes fall on the strap from picking shelf, and cigarettes move forwards in the strap. Picking condition of different picking models can be seen intuitively through simulation program. This program still can offer picking information of simulation, such as the numbers of picking orders and picking cigarette, picking time, efficiency, etc. The data of order come from the real work data of cigarette distribution center, and take on quite representation. At the same time, we can also update the order data. Picking attribute such as speed of belt, the one time of taking cigarette from the shelves and the time of crossing over the shelves side by side, can be changed in simulation program. The influence to picking process of picking parameters and the character of orders, can be analyzed through the comparisons of different picking models.
In the paper, factors of effecting on electronic labeling system, such as the character of order, the position which cigarette put in shelves and three different kinds of picking models, are synthetically considered. Orders are classified by ABC classify of EIQ analysis method, In the different picking model, the result of simulation has been obtained through changing the place in which different kind of cigarettes are put. The result shows that, picking efficiency of the four-order model is great higher than that of the one-order model, and picking efficiency of the two-order model is middle between them. But they aren't absolutely linear relationships, and they have been affected by the character of orders and the position of cigarettes etc. In the same
picking models, after orders are classified by ABC, the efficiency of putting in the
right place is higher than that which is not classified by ABC. Not considering
supplying with cigarette, the efficiency of big orders is much higher than that of little
orders.
引文
[1] http://56products.com/enterprise/chanppin/AIOI.htm
[2] http://www.lft.com.tw/caps/overview_gb.htm
[3] http://ablepick.com/xtjsl.htm
[4] Elsayed, E. A., "Algorithms for optimal material handling in automatic warehousing systems," International Journal of Production Research, 1981,19(5): 525-535.
[5] Hwang, H., and Lee, M. K.,"Order hatching algorithms for a man-on-board automated storage and retrieval system," Engineering Costs and Production Economics, 1988, 13(1): 285-294.
[6] Goetschalckx, M., and Ratliff, H. D., "An efficient algorithm to cluster order picking items in a wide aisle," Engineering Costs and Production Economics, 1988, 13(1): 263-271.
[7] Koster, M. B. M. DE, Poort, E. S. VAN DER, and Wolters, M., "Efficient orderbatching methods in warehouses," International Journal of Production Research, 1999, 37(7): 1479-1504.
[8] 文坚，马士华．基于时间延迟的订单分批策略研究，物流技术与应用．2005．4：92-95．
[9] 马士华，文坚．基于时间延迟的订单分批策略研究．工业工程与管理．2004．6：1-4．
[10] Che-Hung Lin, Iuan-Yuan Lu. The procedure of determining the order picking strategies in distribution center. International Journal of Production Economics. April 20, 1999, 60-61: 301-307.
[11] Chih-Ming Hsu, Kai-Ying Chen, Mu-Chen Chen. Batching orders in warehouses by minimizing travel distance with genetic algorithms. Computers in Industry. February, 2005, 56(2): 169-178.
[12] Mu-Chen Chen, Cheng-Lung Huang, Kai-Ying Chen, Hsiao-Pin Wu. Aggregation of orders in distribution centers using data mining. Expert Systems with Applications. April, 2005, 28(3): 453-460.
[13] Elizabeth Jewkes, Chulung Lee, Ray Vickson. Product location, allocation and server home base location for an order picking line with multiple servers. Computers and Operations Research. April, 2004, 31 (4): 623-636.
[14] 周晓光，王国维．包裹分拣机供包模型及计算机仿真．物流技术．2001．3：3-6．
[15] Loon C. Tang, Ek-Peng Chew. Order Picking Systems: Batching and Storage Assignment Strategies. Computers & Industrial Engineering. December, 1997, 33(3-4): 817-820.
[16] Ek Peng Chew, Loon Ching Tang. Travel time analysis for general item location assignment in a rectangular warehouse. European Journal of Operational Research. February 1, 1999, 112(3): 582-597.
[17] 傅静芳．配送中心的拣货作业模式研究．物流技术．2005．3：41-42．
[18] 李诗珍，王转，陶仲卿．配送中心拣货方式决策模型及应用．江汉石油学院学报(社科版)．2002．6，4(2)：50—51．
[19] 李诗珍，王长建．优化配送中心订单拣取路径的一种动态规划方法．运筹与管理．2003．6，12(3)：117-121．
[20] Richard L. Daniels, Jeffrey L. Rummel, Robert Schantz. A model for warehouse order picking. European Journal of Operational Research. February 16, 1998, 105(1): 1-17.
[21] Kees Jan Roodbergen, Rene de Koster. Routing order pickers in a warehouse with a middle aisle. European Journal of Operational Research. August 16, 2001, 133(1): 32-43.
[22] H. Brynzer, M. I. Johansson. Storage location assignment: Using the product structure to reduce order picking times. International Journal of Production Economics. December, 1996, 46-47: 595-603.
[23] Charles G. Petersen, Gerald Aase. A comparison of picking, storage, and routing policies in manual order picking. International Journal of Production Economics. November 8, 2004, 92(1): 11-19.
[24] 杨华，李欣，钟敏．自动化立体仓库堆垛机拣选作业调度研究．起重运输机械．2005．3：23-26．
[25] 宁春林．基于蚁群算法立体仓库拣选路径优化问题的研究．山东大学硕士学位论文．2002．11．
[26] 汪长飚．基于eM-Plant的自动化立体仓库分拣作业的优化与仿真．武汉理工大学硕士学位论文．2005．06．
[27] 宁春林，田国会，尹建芹，路飞．Max-Min蚁群算法在固定货架拣选路径优化中的应用．山东大学学报(工学版)．2003．12，33(6)：676-680．
[28] 田伟，田国会，张攀，李晓磊．应用改进LK算法求解固定货架拣选优化问题。计算机应 用．2004．06，24(6)：167-170．
[29] 张丹羽，廖莉．物流系统概论．山东大学出版社．2003．
[30] 朱新民，林敏晖．物流采购管理．机械工业出版社．2004，7．
[31] 李诗珍，王转，张克诚．配送中心拣货作业中的订单分批策略研究．物流技术．2002．4：31-33．
[32] 李诗珍．配送中心管理调度模型研究—拣货系统的研究．北京科技大学硕士学位毕业论文．2002．
[33] 苏志远．电子标签辅助拣选研究．物流技术．2005．9：51-53．
[34] 张燕云．离散事件系统建模与仿真．清华大学出版社．1991．
[35] 顾启泰．离散事件系统建模与仿真．清华大学出版社．1999，8．
[36] 贾连兴．仿真技术与软件．国防工业出版社．2006，8．
[37] 台北市机械工业研究所．物流中心分拣作业．台北经济部商业司．1998．
[38] 王乐乐．EIQ分析方法及其在配送中心规划中的应用．北京科技大学硕士学何论文．2005，2．
[39] 李庆磊．物流中心分拣策略的研究与应用．山东大学硕士学位论文．2006，4．
[40] 范启印．成品卷烟配送中心分拣系统的研究．昆明理工大学硕士学位毕业论文．2004．
[2] http://www.lft.com.tw/caps/overview_gb.htm
[3] http://ablepick.com/xtjsl.htm
[4] Elsayed, E. A., "Algorithms for optimal material handling in automatic warehousing systems," International Journal of Production Research, 1981,19(5): 525-535.
[5] Hwang, H., and Lee, M. K.,"Order hatching algorithms for a man-on-board automated storage and retrieval system," Engineering Costs and Production Economics, 1988, 13(1): 285-294.
[6] Goetschalckx, M., and Ratliff, H. D., "An efficient algorithm to cluster order picking items in a wide aisle," Engineering Costs and Production Economics, 1988, 13(1): 263-271.
[7] Koster, M. B. M. DE, Poort, E. S. VAN DER, and Wolters, M., "Efficient orderbatching methods in warehouses," International Journal of Production Research, 1999, 37(7): 1479-1504.
[8] 文坚，马士华．基于时间延迟的订单分批策略研究，物流技术与应用．2005．4：92-95．
[9] 马士华，文坚．基于时间延迟的订单分批策略研究．工业工程与管理．2004．6：1-4．
[10] Che-Hung Lin, Iuan-Yuan Lu. The procedure of determining the order picking strategies in distribution center. International Journal of Production Economics. April 20, 1999, 60-61: 301-307.
[11] Chih-Ming Hsu, Kai-Ying Chen, Mu-Chen Chen. Batching orders in warehouses by minimizing travel distance with genetic algorithms. Computers in Industry. February, 2005, 56(2): 169-178.
[12] Mu-Chen Chen, Cheng-Lung Huang, Kai-Ying Chen, Hsiao-Pin Wu. Aggregation of orders in distribution centers using data mining. Expert Systems with Applications. April, 2005, 28(3): 453-460.
[13] Elizabeth Jewkes, Chulung Lee, Ray Vickson. Product location, allocation and server home base location for an order picking line with multiple servers. Computers and Operations Research. April, 2004, 31 (4): 623-636.
[14] 周晓光，王国维．包裹分拣机供包模型及计算机仿真．物流技术．2001．3：3-6．
[15] Loon C. Tang, Ek-Peng Chew. Order Picking Systems: Batching and Storage Assignment Strategies. Computers & Industrial Engineering. December, 1997, 33(3-4): 817-820.
[16] Ek Peng Chew, Loon Ching Tang. Travel time analysis for general item location assignment in a rectangular warehouse. European Journal of Operational Research. February 1, 1999, 112(3): 582-597.
[17] 傅静芳．配送中心的拣货作业模式研究．物流技术．2005．3：41-42．
[18] 李诗珍，王转，陶仲卿．配送中心拣货方式决策模型及应用．江汉石油学院学报(社科版)．2002．6，4(2)：50—51．
[19] 李诗珍，王长建．优化配送中心订单拣取路径的一种动态规划方法．运筹与管理．2003．6，12(3)：117-121．
[20] Richard L. Daniels, Jeffrey L. Rummel, Robert Schantz. A model for warehouse order picking. European Journal of Operational Research. February 16, 1998, 105(1): 1-17.
[21] Kees Jan Roodbergen, Rene de Koster. Routing order pickers in a warehouse with a middle aisle. European Journal of Operational Research. August 16, 2001, 133(1): 32-43.
[22] H. Brynzer, M. I. Johansson. Storage location assignment: Using the product structure to reduce order picking times. International Journal of Production Economics. December, 1996, 46-47: 595-603.
[23] Charles G. Petersen, Gerald Aase. A comparison of picking, storage, and routing policies in manual order picking. International Journal of Production Economics. November 8, 2004, 92(1): 11-19.
[24] 杨华，李欣，钟敏．自动化立体仓库堆垛机拣选作业调度研究．起重运输机械．2005．3：23-26．
[25] 宁春林．基于蚁群算法立体仓库拣选路径优化问题的研究．山东大学硕士学位论文．2002．11．
[26] 汪长飚．基于eM-Plant的自动化立体仓库分拣作业的优化与仿真．武汉理工大学硕士学位论文．2005．06．
[27] 宁春林，田国会，尹建芹，路飞．Max-Min蚁群算法在固定货架拣选路径优化中的应用．山东大学学报(工学版)．2003．12，33(6)：676-680．
[28] 田伟，田国会，张攀，李晓磊．应用改进LK算法求解固定货架拣选优化问题。计算机应 用．2004．06，24(6)：167-170．
[29] 张丹羽，廖莉．物流系统概论．山东大学出版社．2003．
[30] 朱新民，林敏晖．物流采购管理．机械工业出版社．2004，7．
[31] 李诗珍，王转，张克诚．配送中心拣货作业中的订单分批策略研究．物流技术．2002．4：31-33．
[32] 李诗珍．配送中心管理调度模型研究—拣货系统的研究．北京科技大学硕士学位毕业论文．2002．
[33] 苏志远．电子标签辅助拣选研究．物流技术．2005．9：51-53．
[34] 张燕云．离散事件系统建模与仿真．清华大学出版社．1991．
[35] 顾启泰．离散事件系统建模与仿真．清华大学出版社．1999，8．
[36] 贾连兴．仿真技术与软件．国防工业出版社．2006，8．
[37] 台北市机械工业研究所．物流中心分拣作业．台北经济部商业司．1998．
[38] 王乐乐．EIQ分析方法及其在配送中心规划中的应用．北京科技大学硕士学何论文．2005，2．
[39] 李庆磊．物流中心分拣策略的研究与应用．山东大学硕士学位论文．2006，4．
[40] 范启印．成品卷烟配送中心分拣系统的研究．昆明理工大学硕士学位毕业论文．2004．