نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشگاه شهید بهشتی.

2 دانشجوی کارشناسی ارشد، دانشگاه شهید بهشتی.

چکیده

در این پژوهش، موضوع تخصیص کالا در شرایطی که تنوع کالا و تعداد مشتریان زیاد است، مورد بررسی قرار می‌گیرد. گسترش سطح دسترسی به اینترنت و افزایش میل به خرید آنلاین، تعداد مشتریان را افزایش داده است. در شرایطی که تنوع کالا و تعداد مشتریان زیاد است، حل مسائلی مانند تحویل به موقع کالا یا خدمات، انتخاب و تعیین سفارش‌ها در انبارهای غیر متمرکز و موضوع تخصیص انبار به مشتریان دشوار است. برای حل این چالش‌ها، استفاده از مدل‌سازی ریاضی با روش‌های حل فراابتکاری پیشنهاد شده است. اما به دلیل تعداد زیاد حالت‌های تخصیص، حل مدل‌های ریاضی بسیار پیچیده و زمانبر است. با بهبود قدرت محاسباتی و فضای ذخیره سازی، روش‌های مبتنی بر داده برای حل این چالش‌ها توسط محققان مورد مطالعه قرار گرفته است. در این تحقیق، یک راه‌حل ترکیبی مبتنی بر داده که از داده‌کاوی و مدل سازی‌ریاضی استفاده می‌کند، تا تنوع کالاها و تعداد مشتریان را مدیریت کند، پیشنهاد شده است که در زمان کمتری مدل‌های ریاضی تخصیص را با دقت بالا، حل می‌کند. این روش روی داده‌های «دیجی کالا» پیاده سازی شده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A Hybrid Data-Mining Algorithm and Data-Driven Supply Chain Modeling for Allocation Goods to Warehouses and Warehouse Service to Customers

نویسندگان [English]

  • Sadra Ahmadi 1
  • Reza Yousefpour 2

1 Assistant Professor, Shahid Beheshti University.

2 Master Student, Shahid Beheshti University.

چکیده [English]

In this research, the issue of product allocation in a situation that there are a large number customers and goods are various, is investigated. Expanding the level of Internet access and increasing the desire of online shopping, raise the number of customers. In a situation where there is a great variety of goods and a large number of customers, it is difficult to solve issues such as on-time delivery of goods or services, selection and ordering in decentralized warehouses, and the issue of warehouse allocation to customers. To solve these challenges, the use of mathematical modeling with meta-heuristic solution methods has been proposed so far, but due to the large number of allocation modes, solving mathematical models is very complex and it takes time. With the improvement of computing power and storage space, data-driven methods have been studied by researchers to solve these challenges. In this study, a hybrid data-driven solution that uses data mining and mathematical modeling to manage the variety of goods and the number of customers has been proposed, that manages the variety of goods and the number of customers, and can solve mathematical models in less time. This method has been implemented on the data of "DigiKala".

کلیدواژه‌ها [English]

  • Supply Chain
  • Warehouse Location
  • Data Mining
  • Warehouse Allocation
  • Clustering
  1. Abdulkader, M. M. S., Gajpal, Y., & ElMekkawy, T. Y. (2018). Vehicle routing problem in omni-channel retailing distribution systems. International Journal of Production Economics, 196, 43-55.
  2. Arnaout, J.-P., ElKhoury, C., & Karayaz, G. (2020). Solving the multiple level warehouse layout problem using ant colony optimization. Operational Research, 20(1), 473-490.
  3. Ballesteros-Riveros, F. A., Arango-Serna, M. D., Adarme-Jaimes, W., & Zapata-Cortes, J. A. (2019). Storage allocation optimization model in a Colombian company. Dyna, 86(209), 255-260.
  4. Billal, M. M., & Hossain, M. (2020). Multi-Objective optimization for multi-product multi-period four Echelon supply chain problems under uncertainty. Journal of Optimization in Industrial Engineering, 13(1), 1-17.
  5. Chen, C., Liu, J., Li, Q., Wang, Y., Xiong, H., & Wu, S. (2017). Warehouse site selection for online retailers in inter-connected warehouse networks. Paper presented at the 2017 IEEE International Conference on Data Mining (ICDM).
  6. Egas, C. A. (2012). Methodology for Data Mining Customer Order History for Storage Assignment. Ohio University,
  7. Fattahi, P., Sarhadi, H., & Pourfathi, A. (2008). Solving P-Median Problem based on Ant Colony Metaheuristic. Paper presented at the 6th Internatioan Industrial Engineering Conference.
  8. Ghojavand, H., Zandieh, M., & Dorri Nokarani, B. (2011). Application of meta-heuristic algorithms to the logistic integration network distribution model. Journal of Industrial Management Perspective, 1(Issue 3, Autumn 2011), 99-119. Retrieved from (In Persian)
  9. González-Reséndiz, J., Arredondo-Soto, K. C., Realyvásquez-Vargas, A., Híjar-Rivera, H., & Carrillo-Gutiérrez, T. (2018). Integrating simulation-based optimization for lean logistics: a case study. Applied sciences, 8(12), 2448.
  10. Guerriero, F., Pisacane, O., & Rende, F. (2015). Comparing heuristics for the product allocation problem in multi-level warehouses under compatibility constraints. Applied Mathematical Modelling, 39(23-24), 7375-7389.
  11. Hou, Z. (2020). The optimization of automated goods dynamic allocation and warehousing model. Компьютерная оптика, 44(5).
  12. 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.
  13. Izdebski, M., Jacyna-Gołda, I., & Wasiak, M. (2016). The application of genetic algorithm for warehouse location in logistic network. Journal of KONES, 23.
  14. Jiao, Y.-l., Xing, X.-c., Zhang, P., Xu, L.-c., & Liu, X.-R. (2018). Multi-objective storage location allocation optimization and simulation analysis of automated warehouse based on multi-population genetic algorithm. Concurrent Engineering, 26(4), 367-377.
  15. Khishtandar, S., Zandieh, M., Dorri Nokarani, B., & Ranaei Siadat, S. O. (2016). Evolutionary Algorithms for Location Allocation Biomethane Supply Chain Problem. Journal of Industrial Management Perspective, 6(3, Autumn 2016), 29-54. Retrieved from https://www.magiran.com/paper/1701062 (In Persian)
  16. Li, L. (2007). Supply Chain Management: Concepts, Techniques and Practices: Enhancing the Value through Collaboration: World scientific publishing company.
  17. Li, X., Zheng, Y., Zhou, Z., & Zheng, Z. (2019). Demand prediction, predictive shipping, and product allocation for large-scale e-commerce. Predictive Shipping, and Product Allocation for Large-Scale E-Commerce (March 12, 2019).
  18. Lorenc, A., Kuźnar, M., & Lerher, T. (2021). Solving product allocation problem (PAP) by using ANN and clustering. FME Transactions, 49(1), 206-213.
  19. Lorenc, A., & Lerher, T. (2019). Effectiveness of product storage policy according to classification criteria and warehouse size. FME Transactions, 47(1), 142-150.
  20. Lotfi, R., & Amin Nayeri, M. (2016). Multi-Objective Capacitated Facility Location with Hybrid Fuzzy Simplex and Genetic Algorithm Approach. Journal of Industrial Engineering Research in Production Systems, 4(7), 81-91. (In Persian)
  21. Min, H., & Zhou, G. (2002). Supply chain modeling: past, present and future. Computers & Industrial Engineering, 43(1-2), 231-249.
  22. Mohaghar, A., & Ariaee, S. (2017). Locating using Geographical Information System and Weighted Maximal Covering Model. Journal of Industrial Management Perspective, 7(2, Summer 2017), 9-32. (In Persian)
  23. Mohammed, A. M., & Duffuaa, S. O. (2020). A tabu search based algorithm for the optimal design of multi-objective multi-product supply chain networks. Expert Systems with Applications, 140, 112808.
  24. Moradi, H., Shetab Bushehri, N., Kourank Beheshti, A., & Poorzahedy, H. (2010). Location of Competitive Service Centers for Reducing City Traffic Case Study: Health Centers of the City of Isfahan. Journal of Production and Operations Management, 1(1), 31-52. (In Persian)
  25. Olafsson, S., Li, X., & Wu, S. (2008). Operations research and data mining. European journal of operational research, 187(3), 1429-1448.
  26. Pang, K.-W., & Chan, H.-L. (2017). Data mining-based algorithm for storage location assignment in a randomised warehouse. International Journal of Production Research, 55(14), 4035-4052.
  27. Rad, S. Y. B., Rad, M. A. B., Desa, M. I., Behnam, S., & Lessanibahri, S. (2010). A non-linear model for the classification of stored items in supply chain management. Paper presented at the 2010 International Symposium on Information Technology.
  28. Rosenwein, M. B. (1994). An application of cluster analysis to the problem of locating items within a warehouse. IIE transactions, 26(1), 101-103.
  29. Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., & Shankar, R. (2008). Designing and managing the supply chain: concepts, strategies and case studies: Tata McGraw-Hill Education.
  30. Van Gils, T., Ramaekers, K., Caris, A., & de Koster, R. B. (2018). Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review. European journal of operational research, 267(1), 1-15.
  31. Wang, W., Gao, J., Gao, T., & Zhao, H. (2017). Optimization of Automated Warehouse Location Based on Genetic Algorithm. Paper presented at the Proceedings of the 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017), Sanya, China.
  32. Zapata-Cortes, J. A., Arango-Serna, M. D., Serna-Urán, C. A., & Ortíz-Vasquez, L. F. (2021). Multi-Objective Product Allocation Model in Warehouses. In Techniques, Tools and Methodologies Applied to Quality Assurance in Manufacturing (pp. 249-268): Springer.
  33. Zhang, Y., Lin, W.-H., Huang, M., & Hu, X. (2021). Multi-warehouse package consolidation for split orders in online retailing. European journal of operational research, 289(3), 1040-1055.
  34. Zhu, S., Hu, X., Huang, K., & Yuan, Y. (2021). Optimization of product category allocation in multiple warehouses to minimize splitting of online supermarket customer orders. European journal of operational research, 290(2), 556-571.