Supply Chain Management is the process of integrating and utilizing suppliers, manufacturers, distribution centers, and retailers; so that products are produced and delivered to the end-users at the right quantities and at the right time, while minimizing costs and satisfying customer requirements. From this definition, a supply chain includes three sub-systems: procurement, production, and distribution. The overall performance of a supply-chain is influenced significantly by the decisions taken in its production-distribution plan. A production-distribution plan excludes the procurement activities and integrates the decisions in production, transport and warehousing as well as inventory management. Hence, one key issue in the performance evaluation of a supply network is the modeling and optimization of production-distribution plan considering its actual complexity. This paper develops a mixed integer formulation for a two-echelon supply network that expands the previously reported production-distribution models through the integration of Aggregate Production Plan and Distribution Plan as well as considering the real-world variables and constraints. A Genetic Algorithm is designed for the optimization of the developed model. The methodology will be then implemented to solve a real-life problem incorporating multiple time periods, multiple products, multiple manufacturing plants, multiple warehouses and multiple end-users. To demonstrate the capability of the approach, the validation and performance evaluation of this model will be finally studied for the presented case study.
A mixed integer bi-level DEA model for bank branch performance evaluation by Stackelberg approach
