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A possibilistic linear programming model for supply chain network design under uncertainty

A possibilistic linear programming model for supply chain network design under uncertainty In this paper, we develop a possibilistic linear programming model for supply chain network design with imprecise inputs: market demands, supplied quantities, transportation costs, opening costs, treatment and storage costs are modelled as fuzzy numbers. An efficient possibilistic linear programme is constructed with fuzzy objective function that minimizes the sum of investment costs and operating costs of the supply chain. A method for solving the programming problem with fuzzy parameters is proposed. Application to a supply chain problem at a European textile company illustrates our methodology. Numerical results show that the performance of the proposed model in handling data uncertainty is better when compared to a classical deterministic model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png IMA Journal of Management Mathematics Oxford University Press

A possibilistic linear programming model for supply chain network design under uncertainty

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Publisher
Oxford University Press
Copyright
The authors 2012. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.
Subject
Articles
ISSN
1471-678X
eISSN
1471-6798
DOI
10.1093/imaman/dps012
Publisher site
See Article on Publisher Site

Abstract

In this paper, we develop a possibilistic linear programming model for supply chain network design with imprecise inputs: market demands, supplied quantities, transportation costs, opening costs, treatment and storage costs are modelled as fuzzy numbers. An efficient possibilistic linear programme is constructed with fuzzy objective function that minimizes the sum of investment costs and operating costs of the supply chain. A method for solving the programming problem with fuzzy parameters is proposed. Application to a supply chain problem at a European textile company illustrates our methodology. Numerical results show that the performance of the proposed model in handling data uncertainty is better when compared to a classical deterministic model.

Journal

IMA Journal of Management MathematicsOxford University Press

Published: Apr 4, 2013

Keywords: supply chain network design possibilistic linear programming fuzzy number uncertainty

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