A possibilistic decision model for new product supply chain design

A possibilistic decision model for new product supply chain design

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Article ID: iaor20084423
Country: Netherlands
Volume: 177
Issue: 2
Start Page Number: 1044
End Page Number: 1061
Publication Date: Mar 2007
Journal: European Journal of Operational Research
Authors: ,
Keywords: fuzzy sets, heuristics: genetic algorithms, innovation
Abstract:

This paper models supply chain (SC) uncertainties by fuzzy sets and develops a possibilistic SC configuration model for new products with unreliable or unavailable SC statistical data. The supply chain is modeled as a network of stages. Each stage may have one or more options characterized by the cost and lead-time required to fulfill required functions and may hold safety stock to prevent an inventory shortage. The objective is to determine the option and inventory policy for each stage to minimize the total SC cost and maximize the possibility of fulfilling the target service level. A fuzzy SC model is developed to evaluate the performance of the entire SC and a genetic algorithm approach is applied to determine near-optimal solutions. The results obtained show that the proposed approach allows decision makers to perform trade-off analysis among customer service levels, product cost, and inventory investment depending on their risk attitude. It also provides an alternative tool to evaluate and improve SC configuration decisions in an uncertain SC environment.

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