Distribution system optimization in the field of cosmetic products: A case study

Document Type: Research Paper


Department of Information Technology Management, Kharazmi University, Tehran, Iran


Considering the importance of distributing and selling products through various channels and their different costs, choosing a distribution channel and planning and controlling their operations in order to serve customers is a very important and high efficient affair. Regarding a generalized allocation model, this article has attempted to present a new type of distribution system in the supply chain, so that each one of the distributors can focus more on the retailers and prevent the conflict between distribution channels. A nonlinear multi-purpose mixed-integer programming model with a robust optimization approach has been considered, so that while minimizing distribution costs, the allocation ratio of orders to retailers was also maximized. In the phase of solving the designed model, at first the initial nonlinear model was converted into a linear model and as the model was multi-purpose, the metric LP method has been used for solving small dimensions. Then, to reinforce the model against uncertain parameter changes, the model has been reinforced by considering the demand as an uncertain parameter. The integrated QFD/AHP approach has also been used to evaluate and rank distribution methods, taking into account the important characteristics of the distributors and the retailers' desired criteria regarding distributors.


Main Subjects

Braglia, M., & Petroni, A. (2000). A quality assurance‐oriented methodology for handling trade‐offs in supplier selection. International Journal of Physical Distribution & Logistics Management.
Cavusgil, S. T., Deligonul, S., & Zhang, C. (2004). Curbing foreign distributor opportunism: An examination of trust, contracts, and the legal environment in international channel relationships. Journal of International Marketing12(2), 7-27.
Ernst, H., Hoyer, W. D., & Rübsaamen, C. (2010). Sales, marketing, and research-and-development cooperation across new product development stages: implications for success. Journal of Marketing74(5), 80-92.
Holzapfel, A., Kuhn, H., & Sternbeck, M. G. (2018). Product allocation to different types of distribution center in retail logistics networks. European Journal of Operational Research264(3), 948-966.
Hua, X., Hu, X., & Yuan, W. (2016). Research optimization on logistics distribution center location based on adaptive particle swarm algorithm. Optik127(20), 8443-8450.
Kumar, M., Vrat, P., & Shankar, R. (2006). A fuzzy programming approach for vendor selection problem in a supply chain. International Journal of Production Economics101(2), 273-285.
Kumar, V., & Reinartz, W. J. (2006). Customer relationship management: A databased approach. Hoboken, NJ: Wiley.
Moriarty, R. T., & Moran, U. (1990). Managing hybrid marketing systems. Harvard Business Review68(6), 146.
Ren, S., Choi, T. M., Lee, K. M., & Lin, L. (2020). Intelligent service capacity allocation for cross-border-E-commerce related third-party-forwarding logistics operations: A deep learning approach. Transportation Research Part E: Logistics and Transportation Review134, 101834.
Vieira, J. G. V., Toso, M. R., da Silva, J. E. A. R., & Ribeiro, P. C. C. (2017). An AHP-based framework for logistics operations in distribution centres. International Journal of Production Economics187, 246-259.
Vorhies, D. W., Harker, M., & Rao, C. P. (1999). The capabilities and performance advantages of market‐driven firms. European journal of marketing.
Zhao, X., Huo, B., Flynn, B. B., & Yeung, J. H. Y. (2008). The impact of power and relationship commitment on the integration between manufacturers and customers in a supply chain. Journal of operations management26(3), 368-388.