Advertisement

Journal of Intelligent Manufacturing

, Volume 28, Issue 1, pp 111–129 | Cite as

A RFID-based storage assignment system for enhancing the efficiency of order picking

  • K. L. Choy
  • G. T. S. Ho
  • C. K. H. Lee
Article

Abstract

In today’s time-sensitive markets, effective storage policies are widely accepted as a means for improving the efficiency of order picking. As a result of customization, the variety of products handled by a warehouse has increased, making storage location assignment problems more complicated. Different approaches have been proposed by researchers for improving storage assignment and order picking. However, many industrial practitioners find it difficult to adopt such approaches due to complexity and high associated costs. In particular, small and medium enterprises (SMEs), that generally, lack resources and who have staff members with weak artificial intelligence backgrounds, still rely on experience when assigning storage locations for diverse products. In these circumstances, the quality of decision making cannot be guaranteed. In view of this, an intelligent system which can be easily adopted by SMEs is designed to improve storage location assignment problems. The proposed system, an RFID-based storage assignment system (RFID-SAS), is a rule-based system incorporating radio frequency identification (RFID) provides decision support for storage assignment in a warehouse. Unlike many existing situations, RFID tags are attached to products at the item level instead of at the pallet level. As the knowledge embedded in the system is represented in the form of rules, evaluation is important and is outlined in this paper. The effectiveness of the system is verified by means of a case study in which the system is implemented in a typical SME specializing in machinery manufacturing. The results illustrate that RFID-SAS can enhance the efficiency of order picking in a warehouse.

Keywords

Storage location assignment Order picking Small and medium enterprise RFID Fuzzy logic 

Notes

Acknowledgments

The authors would like to thank the Research Office of the Hong Kong Polytechnic University for supporting this project (Project Code: G-UC66).

References

  1. Adali, M. R., Taşkin, M. F., & Taşkin, K. (2009). Selecting the optimal shift numbers using fuzzy control model: A paint factory’s facility application. Journal of Intelligent Manufacturing, 20(2), 267–272.CrossRefGoogle Scholar
  2. Aliev, R. A., Fazlollahi, B., Guirimov, B. G., & Aliev, R. R. (2007). Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management. Information Sciences, 177(20), 4241–4255.CrossRefGoogle Scholar
  3. Bellman, R., & Giertz, M. (1973). On the analytic formalism of the theory of fuzzy sets. Information Sciences, 5, 149–156.CrossRefGoogle Scholar
  4. Bessenouci, H. N., Sari, Z., & Ghomri, L. (2012). Metaheuristic based control of a flow rack automated storage retrieval system. Journal of Intelligent Manufacturing, 23(4), 1157–1166.CrossRefGoogle Scholar
  5. Bosma, R., van den Berg, J., Kaymak, U., Udo, H., & Verreth, J. (2012). A generic methodology for developing fuzzy decision models. Expert Systems with Applications, 39(1), 1200–1210.CrossRefGoogle Scholar
  6. Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47(1), 139–159.CrossRefGoogle Scholar
  7. Brynzér, H., & Johansson, M. I. (1996). Storage location assignment: Using the product structure to reduce order picking times. International Journal of Production Economics, 46(47), 595–603.CrossRefGoogle Scholar
  8. Chen, F., Wang, H., Xie, Y., & Qi, C. (2014). An ACO-based online routing metehod for multiple order pickers with congestion consideration in warehouse. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-0871-1.
  9. Chiang, D. M. H., Lin, C. P., & Chen, M. C. (2011). The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres. Enterprise Information Systems, 5(2), 219–234.CrossRefGoogle Scholar
  10. Chiang, D. M. H., Lin, C. P., & Chen, M. C. (2012). Data mining based storage assignment heuristics for travel distance reduction. Expert Systems. doi: 10.1111/exsy.12006.
  11. 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.CrossRefGoogle Scholar
  12. Gracía, A., Chang, Y., Abarca, A., & Oh, C. (2007). RFID enhanced MAS for warehouse management. International Journal of Logistics Research and Applications: A Leading Journal of Supply Chain Management, 10(2), 97–107.CrossRefGoogle Scholar
  13. Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1–21.CrossRefGoogle Scholar
  14. Ho, G. T. S., Choy, K. L., & Poon, T. C. (2010). Providing decision support functionality in warehouse management using the RFID-based fuzzy association rule mining approach. In 2010 8th international conference on supply chain management and information systems (SCMIS) IEEE (pp. 1–7).Google Scholar
  15. Hsu, C. M., Chen, K. Y., & Chen, M. C. (2005). Batching orders in warehouses by minimizing travel distance with genetic algorithms. Computers in Industry, 56(2), 169–178.CrossRefGoogle Scholar
  16. Joe, Y. Y., Gan, O. P., & Lewis, F. L. (2014). Multi-commodity flow dynamic resource assignment and matrix-based job dispatching for multi-relay transfer in complex material handling systems (MHS). Journal of Intelligent Manufacturing, 25(4), 681–697.Google Scholar
  17. Juels, A. (2006). RFID security and privacy: A research survey. IEEE Journal on Selected Areas in Communications, 24(2), 381–394.Google Scholar
  18. Karray, F. O., & deSilva, C. (2004). Soft computing and intelligent systems design, theory, tools and applications. Hallow, England: Pearson, Addison Wesley.Google Scholar
  19. Kuo, R. J., & Chang, J. W. (2013). Intelligent RFID positioning system through immune-based feed-forward neural network. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-013-0832-0.
  20. Kwok, S. K., & Wu, K. K. W. (2009). RFID-based intra-supply chain in textile industry. Industrial Management & Data Systems, 109(9), 1166–1178.CrossRefGoogle Scholar
  21. Lam, C. H. Y., Choy, K. L., & Chung, S. H. (2011). A decision support system for facilitate warehouse order fulfillment in cross-border supply chain. Journal of Manufacturing Technology Management, 22(8), 972–983.CrossRefGoogle Scholar
  22. Lam, C. H. Y., Chung, S. H., Lee, C. K. M., Ho, G. T. S., & Yip, T. K. T. (2009). Development of an OLAP based fuzzy logic system for supporting put away decision. International Journal of Engineering Business Management, 1(2), 55–60.Google Scholar
  23. Lim, M. K., Bahr, W., & Leung, S. C. H. (2013). RFID in the warehouse: A literature analysis (1995–2010) of its applications, benefits, challenges and future trends. International Journal of Production Economics, 145(1), 409–430.CrossRefGoogle Scholar
  24. Lyu, J, Jr, Chang, S. Y., & Chen, T. L. (2009). Integrating RFID with quality assurance system-framework and applications. Expert Systems with Applications, 36(8), 10877–10882.CrossRefGoogle Scholar
  25. Maravealkis, E., Bilalis, N., Antoniadis, A., Jones, K. A., & Moustakis, V. (2006). Measuring and benchmarking the innovativeness of SMEs: A three-dimensional fuzzy logic approach. Production Planning and Control, 17(3), 283–292.CrossRefGoogle Scholar
  26. Mcfalane, D., Sarma, S., Chirn, J. L., Wong, C. Y., & Ashton, K. (2003). Auto ID systems and intelligent manufacturing control. Engineering Applications of Artificial Intelligence, 16(4), 365–376.CrossRefGoogle Scholar
  27. Mezgár, I., & Kovács, G. L. (1998). Co-ordination of SME production through a co-operative network. Journal of Intelligent Manufacturing, 9(2), 167–172.CrossRefGoogle Scholar
  28. Mula, J., Poler, R., & Garcia-Sabater, J. P. (2007). Material requirement planning with fuzzy constraints and fuzzy coefficients. Fuzzy Sets and Systems, 158(7), 783–793.CrossRefGoogle Scholar
  29. Petersen, C. G., & Aase, G. (2004). A comparison of picking, storage, and routing policies in manual order picking. International Journal of Production Economics, 92(1), 11–19.CrossRefGoogle Scholar
  30. Poon, T. C., Choy, K. L., Chow, H. K. H., Lau, H. C. W., Chan, F. T. S., & Ho, K. C. (2009). A RFID case-based logistics resource management system for managing order-picking operations in warehouses. Expert Systems with Applications, 36, 8277–8301.CrossRefGoogle Scholar
  31. Poulos, P. N., Rigatos, G. G., Tzafestas, S. G., & Koukos, A. K. (2001). A pareto-optimal genetic algorithm for warehouse multi-objective optimization. Engineering Applications of Artificial Intelligence, 14(6), 737–749.CrossRefGoogle Scholar
  32. Singh, R. K., & Benyoucef, L. (2013). A consensue based group decision making methodology for strategic selection problems of supply chain coordination. Engineering Applications of Artificial Intelligence, 26(1), 122–134.Google Scholar
  33. Suhail, A., & Khan, Z. A. (2009). Fuzzy production control with limited resources and response delay. Computers and Industrial Engineering, 56(1), 433–443.CrossRefGoogle Scholar
  34. Tahera, T., Ibrahim, R. N., & Lochert, P. B. (2008). A fuzzy logic approach for dealing with qualitative quality characteristics of a process. Expert Systems with Applications, 34(4), 2630–2638.CrossRefGoogle Scholar
  35. Tsai, C. Y., Liou, J. J. H., & Huang, T. M. (2008). Using a multiple-GA method to solve the batch picking problem: Considering travel distance and order due time. International Journal of Production Research, 46(22), 6533–6555.CrossRefGoogle Scholar
  36. Tse, Y. K., Tan, K. H., Ting, S. L., Choy, K. L., Ho, G. T. S., & Chung, S. H. (2012). Improving postponement operation in warehouse: An intelligent pick-and-pick decision support system. International Journal of Production Research, 50(24), 7181–7197.CrossRefGoogle Scholar
  37. Vrba, P., Macůrek, F., & Mařík, V. (2008). Using radio frequency identification in agent-based control systems for industrial applications. Engineering Applications of Artificial Intelligence, 21(3), 331–342.CrossRefGoogle Scholar
  38. Yang, P., Miao, L., Xue, Z., & Qin, L. (2013). An integrated optimization of location assignment and storage/retrieval scheduling in multi-shuttle automated storage/retrieval systems. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-013-0846-7.
  39. Zhang, Y., Jiang, P., Huang, G., Qu, T., Zhou, G., & Hong, J. (2012). RFID-enabled real-time manufacturing information tracking infrastructure for extended enterprises. Journal of Intelligent Manufacturing, 23(6), 2357–2366.CrossRefGoogle Scholar
  40. Zhou, J., & Shi, J. (2009). RFID localization algorithms and applications-a review. Journal of Intelligent Manufacturing, 20(6), 695–707.CrossRefGoogle Scholar
  41. Zimmermann, H. J. (1991). Fuzzy set theory and its applications. Boston: Kluwer.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHung Hom, KowloonHong Kong

Personalised recommendations