In this talk, we discuss of an application of nonconvex quadratic programming in Global Supply Chain Management. We examine the example of a multinational corporation that attempts to maximize its global after tax profits by determining the flow of goods, the transfer prices, and the transportation cost allocation between each of its subsidiaries. A bilinear programming formulation has been suggested by Vidal and Goetschalckx (EJOR, 2001) for this problem, where each bilinear term corresponds to the product of two decision variables representing the flow of goods and the transfer price between two subsidiaries respectively. These authors proposed an alternate heuristic, where initial solution is obtained by linearization. In this talk, I will present a new model reducing the number of bilinear terms. and present two implementations of Variable Neighborhood Search (VNS) as well as an exact method based on a branch and cut algorithm. Numerical results are presented.
Group for Research in Decision Analysis