Design and Optimization of a Supply Chain Network for Product Distribution

Abstract

This dissertation is concerned with mathematical modeling and optimization of three-echelon supply chain network design with production plants, warehouses, distribution centers and customer zones. An introduction to supply chain network design is provided followed by literature review. Usually a supply chain is represented by a network that contains some nodes. The nodes of supply chain network are suppliers, plants, warehouses, distribution centers and customers and are connected by arcs with each other. The arcs link the nodes in the direction of product flow. The network design determines the physical configuration and infrastructure of the supply chain. Key decisions are made on the number, locations, size of production plants, warehouses, distribution centers and the assignment of customers to distribution centers, etc. Typical planning horizon is a few years. An efficient supply chain network design is essential for organizations as it aims to minimize the total cost so that the product can reach customer at lowest cost with flexible demand. In order to design supply chain network, an optimization model is developed with a single objective to minimize total cost. The model determines the best locations of the production plants, warehouses and distribution centers to maximize the profit, minimize the total cost while satisfying customer demands. The model also specifies: the amount of raw materials transported to production plants, the number of products transported from production plants to warehouses or distribution centers as well as the number of products shipped to the demand points. In this research, a systematic approach is presented for facility placement, optimal production planning and product transportation across network arcs. An optimization formulation is developed for determination of production size, locations of network nodes and optimal supply chain. The objective function considers minimization of transportation cost, production cost and operational costs for the facilities. The incorporation of budget constraint, delivery mode, cross-route costs, maximum flow by a shipping firm, production capacity of the plants, stocking capacity of owned and rented warehouses and traffic factors on the supply routes in the mathematical model further broadened the problem. Numerous problems have been solved to analyze how the model performs with the changing network characteristics. Computational results for different data sets have revealed that the proposed solution approach and mathematical model is effective. It has been demonstrated that benefits of considering traffic factor, cross-route costs, delivery mode and shipping firm selection etc. during supply chain design phase are significant.