A nonlinear mixed-integer programming model for forward/reverse distribution network design

Distribution affects a firm's cost and customer satisfaction and drives profitability. Besides, due to the implementation of government legislation, companies have been required not only to supply environmentally amicable products but also to be responsible for the returned goods. Therefore, in this paper, we present a nonlinear mixed-integer programming model for a forward/reverse logistics network design, with an emphasis on minimizing the total distribution costs. Specifically, the model addresses the following four issues: (1) determine the optimal number of regional distribution centers (RDC); (2) identify where the RDCs should be located; (3) determine the capacity size of each RDC; (4) determine the total distribution costs. A genetic algorithm is developed to solve the model. The test results give important insight for fostering the decision making process.