A parameter-tuned genetic algorithm for vendor managed inventory model for a case single-vendor single-retailer with multi-product and multi-constraint

Subject Areas : Design of Experiment

javad sadeghi ¹ ( MSc, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran )
ahmad sadeghi ² ( Assistant Professor, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran )
mohammad Saidi mehrabad ³ ( Professor, Department of Industrial Engineering, Iran University of Science and Technology, P.C. 16844, Narmak, Tehran, Iran )

Received: 2011-01-10 Accepted : 2011-03-17 Published : 2011-09-29

Keywords: Multi-product, Genetic Algorithm, vendor managed inventory, Multi-constraint, Parameter tuning,

Abstract :

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).