A parameter-tuned Genetic Algorithm for Vendor Managed Inventory Model for a Case Single-vendor Single-retailer with Multi-product and Multi-constraint

This paper develops a single-vendor single-retailer supply chain for multi-product. The proposed model is based on Vendor Managed Inventory (VMI) approach and vendor uses the retailer s data for better decision making. Number of orders and available capital are the constraints of the model. In this system, shortages are backordered; therefore, the vendor’s warehouse capacity is another limitation of the problem. After the model formulation, an Integer Nonlinear Programming problem will be provided; hence, a genetic algorithm has been used to solve the model. Consequently, order quantities, number of shipments received by a retailer and maximum backorder levels for products have been determined with regard to cost consideration. Finally, a numerical example is presented to describe the sufficiency of the proposed strategy with respect to parameter-tuned by response surface methodology (RSM).

Keywords:

Vendor managed inventory , Genetic algorithm , Multi-constraint , Multi-product , Parameter tuning