Integrated manufacturing/distribution planning decisions with multiple imprecise goals in an uncertain environment

Integrated manufacturing/distribution planning decisions with multiple imprecise goals in an... This work presents a possibilistic linear programming (PLP) method for solving the integrated manufacturing/distribution planning decision (MDPD) problems with multiple imprecise goals in supply chains under an uncertain environment. The imprecise PLP model designed here aims to simultaneously minimize total net costs and total delivery time with reference to available supply, capacities, labor levels, quota flexibility and cost budget constraints at each source, as well as forecast demand and warehouse space at each destination. The proposed method achieves greater computational efficiency by employing the simplified triangular distribution to represent imprecise numbers. An industrial case is used to demonstrate the feasibility of applying the proposed method to a real MDPD problem. Overall, the proposed PLP method provides a practical means of solving the multi-objective MDPD problems in an uncertain environment, and can effectively improve manufacturer/ distributor relationships in a supply chain. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Integrated manufacturing/distribution planning decisions with multiple imprecise goals in an uncertain environment

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Publisher
Springer Netherlands
Copyright
Copyright © 2010 by Springer Science+Business Media B.V.
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-010-9333-9
Publisher site
See Article on Publisher Site

Abstract

This work presents a possibilistic linear programming (PLP) method for solving the integrated manufacturing/distribution planning decision (MDPD) problems with multiple imprecise goals in supply chains under an uncertain environment. The imprecise PLP model designed here aims to simultaneously minimize total net costs and total delivery time with reference to available supply, capacities, labor levels, quota flexibility and cost budget constraints at each source, as well as forecast demand and warehouse space at each destination. The proposed method achieves greater computational efficiency by employing the simplified triangular distribution to represent imprecise numbers. An industrial case is used to demonstrate the feasibility of applying the proposed method to a real MDPD problem. Overall, the proposed PLP method provides a practical means of solving the multi-objective MDPD problems in an uncertain environment, and can effectively improve manufacturer/ distributor relationships in a supply chain.

Journal

Quality & QuantitySpringer Journals

Published: Apr 4, 2010

References

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