Fuzzy multi-objective optimization for multi-site integrated production and distribution planning in two echelon supply chain

This paper addresses an integrated production and distribution planning problem for a two-echelon supply chain network comprising of multiple manufacturers serving multiple selling locations. A novel fuzzy multi-objective mixed integer programming model is formulated considering multi-product, multi...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology Vol. 102; no. 1-4; pp. 635 - 645
Main Authors: Badhotiya, Gaurav Kumar, Soni, Gunjan, Mittal, M. L.
Format: Journal Article
Language:English
Published: London Springer London 01.05.2019
Springer Nature B.V
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ISSN:0268-3768, 1433-3015
Online Access:Get full text
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Summary:This paper addresses an integrated production and distribution planning problem for a two-echelon supply chain network comprising of multiple manufacturers serving multiple selling locations. A novel fuzzy multi-objective mixed integer programming model is formulated considering multi-product, multi-period, and multi-site manufacturing environment. Minimization of total cost, delivery time, and backorder level are the three fuzzy objectives represented by piecewise linear membership function. Three important production and distribution aspects viz. capacity of the heterogeneous transportation, backordering for unfulfilled demand, and set-up cost/time for different products at manufacturing site are incorporated in an integrated manner to represent closeness to the real-life problem. An illustrative example inspired from a real-world case of an automobile industry is taken to demonstrate analytical results of the proposed approach. The outcome of the research indicates the practical applicability of the approach. Sensitivity analysis on objective function values is conducted for analyzing the effect of change in aspiration levels of objective function values on decision maker’s satisfaction level.
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ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-018-3204-2