To most firms, intelligent supply chain decisions are essential to achieve competitiveness in an environment characterized with increasing globalization and relentless changes. As the supply chain concept largely evolved from traditional logistics management, practitioners and researchers have historically focused on the individual processes of a supply chain. A wide array of mathematical models have been developed to tackle such functional issues as inventory level, lead-time performance, delivery performance, customer satisfaction and so on. This research presents a model that aims to evaluate and optimize the overall performance of the supply chain. The key nodes and variables in the model are composed of plant location and production volume, storage location and volume, transportation mode and volume. Optimization of the model is to minimize the total supply chain operation cost, expressed as the sum of production cost, storage cost, transportation cost and lost-sale cost. Our methodology is a three-phased approach. First, we build a mixed integer-programming model of 3-tier supply chain with multi-plant, multi-warehouse, and multi-retailer, multi-period and restricted capacity. This mathematical model is solved by CPLEX/OPL. Due to excessive computation time to reach the Optimal Solution, we introduce the second phase to develop heuristic solutions to reduce the computation time. In the final phase, we evaluate the proposed heuristic solutions. Result analysis shows that the computation time is generally exponentially correlated to the data size in seeking Optimal Solutions, whereas it generally follows the polynomial distribution when the Heuristic Solutions are applied. Consequently, Heuristic Solution is preferred for models with large size data.
Library of Congress Subject Headings
Business logistics--Mathematical models
Hong, Ming, "Solution strategies for a supply chain deterministic model" (2007). Thesis. Rochester Institute of Technology. Accessed from
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