Khullar, Chirag (2021) Development of a warehouse slotting model to improve picking performance. Masters thesis, Concordia University.
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Abstract
Congestion during picking operations in warehouse with mixed aisles (narrow aisles and wide aisles) has rarely been studied in current literature in the context of warehouse slotting (i.e. arrangement of inventory in warehouse). This study aims at improving the picking efficiency of the Asmodee Canada Inc. warehouse. Using a combination of clustering slotting heuristics and popularity-based slotting heuristics, a re-slotting policy was developed. Furthermore, to provide a robust re-slotting with limited number of items moves, a healing technique based on urgency score was developed. Use of a process control chart to monitor the picking performance of Asmodee’s warehouse and hence to signal healing was suggested. Using picking simulation, we find that when the re-slotting heuristics is used, there is substantial reduction in distance travelled of up to 29% and waiting times due to congestion can be reduced by as much as 85%. The healing technique also decreased distance travelled and waiting times. However, as the number of items moved in healing constitute on average less than 5% of the items, such improvements are limited. The distance traveled was reduced by as high as 9.4% in some aggregated orders and waiting time reduction was as high as 29.1%. The techniques developed in this paper will help Asmodee Canada Inc. in improving their picking operations. It will also help to build better strategies for warehouses having mixed aisles, where in aisle congestion is an issue to consider while re-slotting.
| Divisions: | Concordia University > John Molson School of Business > Supply Chain and Business Technology Management |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Khullar, Chirag |
| Institution: | Concordia University |
| Degree Name: | M.S.C.M. |
| Program: | Supply Chain Management |
| Date: | 28 July 2021 |
| Thesis Supervisor(s): | Satir, Ahmet and S. Chauhan, Satyaveer |
| Keywords: | Warehousing, SKU Slotting, Order Picking, Picking Frequency, Item Affinity, Clustering, Healing, Urgency Score, Heuristic, Process Control Chart. |
| ID Code: | 988612 |
| Deposited By: | Chirag Khullar |
| Deposited On: | 29 Nov 2021 16:57 |
| Last Modified: | 29 Nov 2021 16:57 |
References:
AlHalawani, S., & Mitra, N. J. (2015, December). Congestion-aware warehouse flow analysis and optimization. In International Symposium on Visual Computing (pp. 702-711). Springer, Cham.Bataineh, M., & Khasawneh, M. (2016). The Effect of Picking Congestion in Manual Order Picking Environments. Proceedings of 5th Annual World Conference of the Society for Industrial and Systems Engineering, , 256-263.
Bataineh, M. (2017). Minimizing Travel Distance During Warehouse Order Picking Considering Congestion Effect (Doctoral dissertation, State University of New York at Binghamton).
De Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182(2), 481-501.
Gue, K. R., Meller, R. D., & Skufca, J. D. (2006). The effects of pick density on order picking areas with narrow aisles. IIE Transactions, 38(10), 859-868.
Henn, S., Koch, S., & Wäscher, G. (2012). Order batching in order picking warehouses: a survey of solution approaches. In Warehousing in the Global Supply Chain (pp. 105-137). Springer, London.
Heskett, J. L. (1963). Cube-per-order index-a key to warehouse stock location. Transportation and Distribution Management, 3(1), 27-31.
Huber, C. (2014). Throughput analysis of manual order picking systems with congestion consideration (Vol. 76). KIT Scientific Publishing.
Jarvis, J. M., & McDowell, E. D. (1991). Optimal product layout in an order picking warehouse. IIE Transactions, 23(1), 93-102.
Klodawski, M., Jachimowski, R., Jacyna-Golda, I., & Izdebski, M. (2018). Simulation analysis of order picking efficiency with congestion situations. International Journal of Simulation Modelling, 17(3), 431-443.
Kofler, M., Beham, A., Wagner, S., Affenzeller, M., & Achleitner, W. (2011, August). Re-warehousing vs. healing: Strategies for warehouse storage location assignment. In 3rd IEEE International Symposium on Logistics and Industrial Informatics (pp. 77-82). IEEE.
Kofler, M., Beham, A., Wagner, S., & Affenzeller, M. (2014). Affinity based slotting in warehouses with dynamic order patterns. In Advanced Methods and Applications in Computational Intelligence (pp. 123-143). Springer, Heidelberg.
Kofler, M., Beham, A., Wagner, S., & Affenzeller, M. (2015). Robust storage assignment in warehouses with correlated demand. In Computational Intelligence and Efficiency in Engineering Systems (pp. 415-428). Springer, Cham.
Lee, I. G., Chung, S. H., & Yoon, S. W. (2020). Two-stage storage assignment to minimize travel time and congestion for warehouse order picking operations. Computers & Industrial Engineering, 139, 106129.
Li, J., Moghaddam, M., & Nof, S. Y. (2016). Dynamic storage assignment with product affinity and ABC classification—a case study. The International Journal of Advanced Manufacturing Technology, 84(9), 2179-2194.
Mantel, R. J., Schuur, P. C., & Heragu, S. S. (2007). Order oriented slotting: a new assignment strategy for warehouses. European Journal of Industrial Engineering, 1(3), 301-316.
Pan, J. C. H., & Shih, P. H. (2008). Evaluation of the throughput of a multiple-picker order picking system with congestion consideration. Computers & Industrial Engineering, 55(2), 379-389.
Pan, J. C. H., Shih, P. H., & Wu, M. H. (2012). Storage assignment problem with travel distance and blocking considerations for a picker-to-part order picking system. Computers & Industrial Engineering, 62(2), 527-535.
Petersen, C. G. (1999). The impact of routing and storage policies on warehouse efficiency. International Journal of Operations & Production Management, 19(10), 1053-1064.
Petersen, C. G., Siu, C., & Heiser, D. R. (2005). Improving order picking performance utilizing slotting and golden zone storage. International Journal of Operations & Production Management, 25(10), 997–1012.
Ruijter, H. (2007). Improved storage in a book warehouse: Design of an efficient tool for slotting the manual picking area at Wolters-Noordhoff (Master's thesis, University of Twente).
Tompkins, J. A., White, J., & Bozer, Y. (1996). Facilities Planning. New York: John Willey & Sons.
Trevino, S., Wutthisirisart, P., Noble, J., & Chang, A. (2009). An approach for order-based warehouse slotting. In IIE Annual Conference. Proceedings (p. 569). Institute of Industrial and Systems Engineers (IISE).
Venkatadri, U., & Kubasad, S. (2012). Estimating travel distances and optimizing product placement for dedicated warehouses with manual picking. Proceedings of the 12th IMHRC, 41, .
Venkatadri, U., Vardarajan, V., & Das, B. (2015). Product placement within a fast-picking tunnel of a distribution centre. The International Journal of Advanced Manufacturing Technology, 76(9-12), 1681-1690.
Yang, P., Zhao, Z., & Guo, H. (2020). Order batch picking optimization under different storage scenarios for e-commerce warehouses. Transportation Research Part E: Logistics and Transportation Review, 136, 101897.
Yu, Y., De Koster, R. B., & Guo, X. (2015). Class‐based storage with a finite number of items: Using more classes is not always better. Production and Operations Management, 24(8), 1235-1247.
Zhang, Y. (2016). Correlated storage assignment strategy to reduce travel distance in order picking. IFAC-PapersOnLine, 49(2), 30-35.
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