MULTI-OBJECTIVE PRODUCTION-DISTRIBUTION PLANNING BASED ON VENDOR MANAGED INVENTORY

Production-distribution (P-D) planning in supply chain leads to a coordination between production and distribution systems, and provides an integrated production and distribution plan in order to create a balance between the costs of production-distribution in supply chain and the level of customer satisfaction. P-D planning and optimization in the context of supply chain management have raised signicant interest for both researchers and practitioners over the past few years. However, demand variances of the retailers may generate the potential challenges for P-D problems, such as increasing supply chain inventory levels and increasing the bullwhip eect. The vendor managed inventory (VMI) can improve supply chain performance by decreasing supply chain inventory levels. It is an important business mode in supply chains where the vendor is responsible to manage the inventory held at the retailer site. In addition, the bullwhip eect can be reduced using VMI. Although many researchers were committed to solve P-D problems in the supply chain environment, they did not pay attention to integrate P-D planning with VMI. In this paper, a multi-objective non-linear mathematical model for production-distribution planning based on vendor-managed inventory (P-D-VMI) is presented for a three-level supply chain, including multiple external suppliers, a single manufacturer, and multiple retailers. The aim of this paper is to minimize the total cost of the manufacture, total cost of the retailers, and total ow times. The inventory of the retailers is managed by the manufacturer, and the common replenishment cycle policy is established between the manufacturer and all the retailers. Then, min-max method and an improved genetic algorithm are utilized to solve the proposed model, and several problems are designed to demonstrate their validation and eciency. Results show that decreasing the retail price elasticity leads to an increase in demand and distribution time along with a reduction in the total cost of the manufacture and retailers, which is more tangible for the retailers. Finally, results conrm the applicability of the proposed model and solution methods.