Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/74257
Title: Multiproduct distribution network design of third party logistics providers with reverse logistics operations
Authors: Bian, W.
Lee, D.-H. 
Dong, M.
Yu, M.
Keywords: Facility location
Genetic algorithm
Multiproduct
Reverse logistics
Third party logistics providers
Issue Date: 2006
Source: Bian, W.,Lee, D.-H.,Dong, M.,Yu, M. (2006). Multiproduct distribution network design of third party logistics providers with reverse logistics operations. 36th International Conference on Computers and Industrial Engineering, ICC and IE 2006 : 4605-4618. ScholarBank@NUS Repository.
Abstract: Reverse logistics, refers to the distribution activities involved in product returns, has recently received growing attention. Due to the difference and interaction between the forward and reverse distribution, how to integrate the forward and reverse channels has become an emerging issue. Many companies currently only have very inefficient, slow and expensive processes for handling returned products. They are either incapable or unwilling to enter the reverse logistics market. Thus, more and more companies are increasingly outsourcing their distribution operations to the third party logistics providers (3PLs). In practice, the 3PLs usually serve a number of clients where the operations of multiple products are involved. Thus, the aspect of multiproduct is necessary to be considered in the distribution network design for the 3PLs. However, almost all the existing research in this field merely focus on the separate reverse distribution and homogeneous product. This paper addresses an integrated forward and reverse distributions network design problem for the 3PLs that involves locating distribution facilities such as warehouse, collection center and hybrid warehouse-collection center, and determining the strategy for distributing the multiple products between the clients of the 3PLs and the customers via the distribution facilities. A genetic algorithm (GA) with two greedy algorithms is used for solution purpose. Computational experiments demonstrate a great deal of promise for this solution method, as high-quality solutions are obtained while expending modest computational effort.
Source Title: 36th International Conference on Computers and Industrial Engineering, ICC and IE 2006
URI: http://scholarbank.nus.edu.sg/handle/10635/74257
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