Quantitative models for decision making in reverse logistics network design

Abstract

In this thesis, we focus on a problem in reverse logistics network design where the aim is locating distribution centers, inspection centers and remanufacturing facilities, determining the acquisition price as well as the amount of returned goods to be collected depending on the unit cost savings and competitor’s acquisition price. The coordination of the forward and reverse flows in the network is also taken into account in order to minimize the transportation costs, fixed costs and used product acquisition costs. A mixed-integer nonlinear programming problem has been formulated and exact algorithms have been suggested to solve it. When the acquisition price is set to a given value, the remaining problem becomes a mixed-integer programming problem which can be solved by Lagrangean relaxation, Benders Decomposition and Cross Decomposition algorithms. The best value of the acquisition price that minimizes the total cost is determined by the Golden Section search and computational results have been reported. Moreover, the effect of fixed cost, capacity as well as unit cost savings on the solution time have been analyzed.