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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Vydáno v:	Annals of operations research Ročník 257; číslo 1-2; s. 95 - 120
Hlavní autoři:	Jindal, Anil, Sangwan, Kuldip Singh
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.10.2017 Springer Springer Nature B.V
Témata:	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
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Abstract	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.
AbstractList	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 [Formula omitted]-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. 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. (ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image) 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 ...-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.
Audience	Academic
Author	Sangwan, Kuldip Singh Jindal, Anil
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Keywords	Environmental assessment Fuzzy mathematical modelling Multi-objective optimization MILP Sustainability Closed-loop supply chain
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SubjectTerms	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
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Title	Multi-objective fuzzy mathematical modelling of closed-loop supply chain considering economical and environmental factors
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