Article ID: | iaor20073180 |
Country: | United Kingdom |
Volume: | 34 |
Issue: | 2 |
Start Page Number: | 346 |
End Page Number: | 366 |
Publication Date: | Feb 2007 |
Journal: | Computers and Operations Research |
Authors: | Evans Gerald W., Ko Hyun Jeung |
Keywords: | heuristics: genetic algorithms, supply & supply chains |
Today's competitive business environment has resulted in increasing cooperation among individual companies as members of a supply chain. Accordingly, third party logistics providers (3PLs) must operate supply chains for a number of different clients who want to improve their logistics operations for both forward and reverse flows. As a result of the dynamic environment in which these supply chains must operate, 3PLs must make a sequence of inter-related decisions over time. However, in the past, the design of distribution networks has been independently conducted with respect to forward and reverse flows. Thus, this paper presents a mixed integer nonlinear programming model for the design of a dynamic integrated distribution network to account for the integrated aspect of optimizing the forward and return network simultaneously. Since such network design problems belong to a class of NP hard problems, a genetic algorithm-based heuristic with associated mumerical results is presented and tested in a set of problems by an exact algorithm. Finally, a solution of a network plan would help in the determination of various resource plans for capacities of material handling equipments and human resources.