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.
Recycling program performance, Technique for order preference by similarity to ideal solution (TOPSIS)
Recycling (Waste (etc.))
Copyright 2004, Surendra M. Gupta
Gupta M. Surendra
Gupta, Surendra M. and Pochampally, Kishore K., "A neural network approach to predict the success of a collection center in a reverse supply chain" (2004). . Paper 10. http://hdl.handle.net/2047/d10013878
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