| A new global supply chain optimization model that includes transfer pricing and transportation cost allocation was developed and solved. The model considers an international multi-echelon structure having internal and external suppliers, manufacturing plants, distribution centers, and customer zones. Along with the traditional decision variables that characterize a logistics system, the model determines two sets of transfer prices and proportions to allocate transportation costs: one between internal suppliers and plants, and the other between plants and distribution centers. The resulting formulation is a bilinear programming problem that is NP-hard. Therefore, a primal heuristic procedure based on successive linear programming was developed and implemented. Computational experiments on small and real-world sized instances produced gaps within 1% of optimality in less than 200 seconds of computation time on average.; To improve the feasible solutions and tight their upper bounds, a global optimization procedure based on the work by Ben-Tal et al. (1994) was implemented. The particular structure of the problem was exploited to calculate dual bounds, and specialized branching rules were implemented. The global optimization procedure improved the feasible solutions and/or tightened the upper bounds in acceptable computation time, showing that the original solutions given by the primal heuristic procedure were closer to optimality than could originally be established.; The computational experiments also revealed a potentially significant impact on the profitability of multinational enterprises. For two of the real-world sized instances, for example, the net profit after tax can be increased by more than 30% by using the model. Several extensions of the model and implications for further research are also discussed in this thesis. |
