MULTIOBJECTIVE MATHEMATICAL PROGRAMMING MODELS OF INTERTEMPORAL MULTICOMMODITY DISTRIBUTION PROBLEMS

This study attempts to develop more realistic and relevant investment planning models for the transportation infrastructure development of commodity distribution problems. Three mixed integer programming models are formulated for intertemporal multicommodity distribution problems. Dynamic characteristics of commodity distribution problems are taken into account: in particular the models are built under dynamic transportation network structure. Intertemporal storage location problems and transportation link addition problems are incorporated into traditional transshipment problems.;The multiobjective programming method is applied for the purpose of generating alternative investment plans appropriate to planning situations and trade-offs between conflicting planning objectives. The multiobjective programming models are formulated for two planning situations: one with the investment capital shortage problem and the other with the induced benefit maximization problem. With minor modification these models can be easily extended to be applicable to a wide class of commodity distribution problems.;Finally, the multiobjective programming model with the investment capital shortage problem is applied to the grain distribution problem in Korea. The results are obtained and analyzed in this study.;The models can be utilized to provide the most effective investment plan for transportation infrastructure development and to evaluate the existing commodity system. They are capable of determining simultaneously the optimal locations, sizes, and activity levels of storage and other handling facilities and of new transportation links as well as the optimal commodity flows through transportation networks.