Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors

The growing concern for sustainability has forced the researchers and managers to incorporate the environmental and social factors along with the economical factors in the design of supply chains. This paper presents the design and optimization of a multi-objective closed-loop supply chain consideri...

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Bibliographic Details
Published in:Annals of operations research Vol. 257; no. 1-2; pp. 95 - 120
Main Authors: Jindal, Anil, Sangwan, Kuldip Singh
Format: Journal Article
Language:English
Published: New York Springer US 01.10.2017
Springer
Springer Nature B.V
Subjects:
Business and Management
Combinatorics
Constraint modelling
Design factors
Design optimization
Dismantling
Economic conditions
Economic models
Fuzzy logic
Integer programming
Linear programming
Mathematical models
Mixed integer
Multiple objective analysis
Operations research
Operations Research/Decision Theory
Parameter uncertainty
Refurbishment
S.I.: Innovative Supply Chain Optimization
Social factors
Supply chains
Sustainability
Theory of Computation
Environmental assessment
Fuzzy mathematical modelling
Multi-objective optimization
MILP
Sustainability
Closed-loop supply chain
ISSN:0254-5330, 1572-9338
Online Access:Get full text
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Summary:The growing concern for sustainability has forced the researchers and managers to incorporate the environmental and social factors along with the economical factors in the design of supply chains. This paper presents the design and optimization of a multi-objective closed-loop supply chain considering the economical and environmental factors with uncertainty in parameters. The proposed network is modeled as fuzzy multi-objective mixed integer linear programming problem considering multi-customer zones, multi-collection centers, multi-disassembly centers, multi-refurbishing centers, multi-external suppliers, and different product recovery processes; to take care for purchasing cost, transportation cost, processing cost, set-up cost, and capacity constraints simultaneously. The model is solved using an interactive ε -constraint method. A case example is solved using LINGO 14.0 to demonstrate the significance and applicability of the developed fuzzy optimization model for closed-loop supply chain.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-016-2219-z