Journal of the Korea Society for Simulation (한국시뮬레이션학회논문지)

The Korea Society for Simulation (한국시뮬레이션학회)
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A Single Order Assignment Algorithm Based on Multi-Attribute for Warehouse Order Picking

물류창고 오더피킹에 있어서 다 속성 기반의 싱글오더 할당 알고리즘

  • Kim, Daebeom (Kangnam University, Industrial System Engineering)
  • Received : 2018.11.05
  • Accepted : 2019.01.17
  • Published : 2019.03.31
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Abstract

Recently, as the importance of warehouses has increased, much efforts are being made to improve the picking process in order to cope with a small amount of high frequency and fast delivery. This study proposes an algorithm to assign orders to pickers in the situation where Single Order Picking policy is used. This algorithm utilizes five attributes related to picking such as picking processing time, elapsed time after receipt of order, inspection/packing workstation situation, picker error, customer importance. A measure of urgency is introduced so that the units of measure for each attribute are the same. The higher the urgency, the higher the allocation priority. In the proposed algorithm, the allocation policy can be flexibly adjusted according to the operational goal of the picking system by changing the weight of each attribute. Simulation experiments were performed on a hypothetical small logistics warehouse. The results showed excellent performance in terms of system throughput and flow time.

최근 물류창고의 중요성이 높아지고 있는 가운데 소량 다빈도 및 빠른 납품에 대응하기 위해 피킹 프로세스 개선에 많은 노력을 기울이고 있다. 본 연구는 개별오더 피킹 정책이 사용되는 상황에서 오더를 피커에게 할당하는 알고리즘을 제시한다. 이 알고리즘에서는 피킹과 관련된 피킹 처리시간, 오더 접수후 경과 시간, 검수/패킹 작업대 상황, 피커 오류, 고객 중요도의 다섯 개의 속성을 동시에 고려한다. 각 속성에 대한 측정값의 단위가 동일하도록 긴급도라는 척도를 도입한다. 긴급도의 값이 클수록 할당 우선순위를 높게 설정한다. 제안한 알고리즘에서는 각 속성의 가중치를 변경함으로써 피킹시스템의 운영목표에 따라 할당 정책을 유연하게 조정할 수 있도록 하였다. 가상의 축소된 물류창고를 대상으로 시뮬레이션 실험을 수행한 결과 시스템 쓰루풋과 흐름시간 측면에서 우수한 성능을 보였다.

Keywords

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Fig. 1. Warehouse layout

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Fig. 2. Flowchart of the proposed algorithm

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Fig. 3. ARENA simulation model for a virtual logistics warehouse

Table 1. Input data of the simulation model

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Table 2. System throughput and flow time under varying the number of pickers and inspectors

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References

  1. Caron, F., Marchet, G. and Perego, A. (2000) "Layout design in manual picking systems: a simulation approach", Integrated Manufacturing Systems, 11, 94-104. https://doi.org/10.1108/09576060010313946
  2. Cho, S. Y., Chang H. Y. and Choe, K. I. (2011) "Ant Colony Optimization Heuristics and Graph Optimization Algorithms of an Aisle-Based Order Picking System", Journal of the Korean Society of Supply Chain Management, 11(2), 13-19.
  3. Jane, C. C. (2000) "Storage location assignment in a distribution center", International Journal of Physical Distribution & Logistics Management, 30(1), 55-71. https://doi.org/10.1108/09600030010307984
  4. Jane, C. C. and Laith, Y. W. (2005) "A clustering algorithm for item assignment in a synchronized zone order picking system", European Journal of Operational Research, 166, 489-496. https://doi.org/10.1016/j.ejor.2004.01.042
  5. Jarvis J. M., and Mcdowell E. D. (1991) "Optimal product layout in an order picking warehouse", IIE Transactions, 23(1), 93-102. https://doi.org/10.1080/07408179108963844
  6. Kim, K. H. (2009) A Study on the Simulation for Optimal Layout Type in the Logistics Center, Myongji University Doctoral Thesis.
  7. Kim, J. H. and Cho, S. W. (2001) "Developing an Order Picking Supporting System and Comparison of Picking Policies", Journal of the Korean Institute of Plant Engineering, 6(2), 109-129.
  8. Kim, G. G. (2018) A study on the distribution standard of Order Picking Operation in warehouse, Inha University Doctoral Thesis.
  9. Koo, P. H. (2008) "Application of Bucket brigades to Order Picking in Warehouses", IE Interfaces, 21(3), 333-342.
  10. Kunder R., and Gudehus T,. (1975) "Average travel times for one-dimensional order picking", Zeitschrift fur Operations Research, 19, 53-72.
  11. Le-Duc, T. and Koster, R. D. (2005) "Determining Number of Zones in a Pick-and-pack Order picking system", Erasmus Research Institute of Management.
  12. Li, J., Lee y. D. and Kim S. K. (2011) "An Evaluation of Routing Methods and the Golden Zone Effect in the Warehouse Order Picking System", Journal of the Korea society for simulation, 20(2), 67-76. https://doi.org/10.9709/JKSS.2011.20.2.067
  13. Oh, D. C. (2018) "A Study on Efficiency Comparison between Order Picking and Batch Picking in Food Distribution Center", Korea Logistics Review, 28(4), 21-33.
  14. Park, B. C. (2011) "Order Picking Performance : Strategies, Issues, and Measure", Journal of the Korean Institute of Industrial Engineers, 37(4), 271-278. https://doi.org/10.7232/JKIIE.2011.37.4.271
  15. Ratliff, H. D. and Rosenthal, A. D. (1983) "Order Picking in rectangular warehouse : a solvable case of the traveling salesman problem", Operations Research, 31, 507-521. https://doi.org/10.1287/opre.31.3.507
  16. Renaud, J. and Ruiz, A. (2008) "Improving Product Location and Order Picking Activities in a Distribution Center", Journal of the Operational Research Society, 59(12), 1603-1613. https://doi.org/10.1057/palgrave.jors.2602514
  17. Tompkins, J. A., White, J. A., Bozer, Y. A., Frazelle, E. H. & Tanchoco, J. M. A. (2003) Facilities Planning, NJ: John Wiley & Sons.
  18. Yim, W. T. (2015) A Simulation Study on Optimal Method for Book Distribution Center, Myongji University Doctoral Thesis.
  19. You, K. C., Kang, K. S. and Rim, S. C. (2012) "Productivity comparison between DPS and DAS for optimal order picking system design of distribution centers", Journal of the Korean Society of Supply Chain Management, 12(2), 111-120.