An integrated model for optimizing pricing, inventory and marketing decisions for fast-moving goods in a multi-channel network

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Document Type : Original Article

Authors

1 PhD student, Malik Ashtar University of Technology

2 Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Malek Ashtar Univercity of Technology,Tehran, Iran

3 Department of Management and Industrial Engineering, Malek Ashtar University of Technology

4 Department of Industrial Engineering, Damghan University

Abstract

One of the elements of the success of any production organization is the optimization of the product distribution channel. Because it affects all the activities of the organization in order to provide the services needed by the customers. Elements such as inventory, marketing decisions, and pricing are effective in optimizing the distribution channel. Although the investigation of these factors alone leads to optimization, but their simultaneous optimization creates global optimal, which has rarely been addressed in past studies. Therefore, this article seeks to present an integrated model for the simultaneous optimization of supply chain elements in the distribution channel. The proposed model is a bi-objective model in which decisions related to marketing; pricing and inventory control are considered. Two internal and external suppliers have been used. In this study, the first objective function seeks to maximize profit and the second objective function is the utility resulting from customer satisfaction. The presented model has been implemented in the supply chain network of Etka chain stores. Inventory management in distribution centers as well as marketing parameters, including product pricing in channels, choosing the best channel for each product, the amount of sales of each product in each channel and finally the amount of discount for products It has been checked in different channels in the proposed model and then it has been solved by LP metric method by GAMS and NSGA2 algorithm. Which shows that the genetic algorithm of NSGA2 in high dimensions provides the desired results in a more appropriate time.

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