Abstract

We propose a neural network approach to evaluate the success potential of a collection center of interest that is being considered for inclusion in a reverse supply chain, using the available linguistic data of collection centers that already exist in the reverse supply chain. The approach is carried out in four phases, as follows. In phase I, we identify the criteria for evaluating the collection center of interest, by each group participating in the reverse supply chain, viz. consumers, governments and executives. Then, in phase II, we use fuzzy ratings of already existing collection centers to construct a neural network that gives impacts of criteria identified for each group in phase I. In phase III, using the impacts obtained in phase II, we employ a fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach to obtain the overall rating of the collection center of interest, as evaluated by each group. Finally, in phase IV, we employ Bordas choice rule to calculate the maximized consensus rating, i.e., success potential of the collection center of interest. An example is considered to illustrate the approach.

Notes

Originally published in the Proceedings of the 2004 Northeast Decision Sciences Institute Conference, Atlantic City, New Jersey, pp. 229-231, March 24-26, 2004

Keywords

Recycling program performance, Technique for order preference by similarity to ideal solution (TOPSIS)

Subject Categories

Recycling (Waste (etc.))

Disciplines

Engineering

Publisher

Omnipress

Publication Date

2004

Rights Information

Copyright 2004, Surendra M. Gupta

Rights Holder

Gupta M. Surendra



Click button above to open, or right-click to save.

Included in

Engineering Commons

Share

COinS