A multi-criteria decision framework for sustainable supplier selection and order allocation using multi-objective optimization and fuzzy approach

With the growing social and environmental concerns, the integration of sustainability in supplier selection and order allocation is of paramount importance. This research presents a holistic multi-phase decision-support framework to solve the sustainable supplier selection and order allocation probl...

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Vydáno v:	Engineering optimization Ročník 54; číslo 6; s. 928 - 948
Hlavní autoři:	Liaqait, Raja Awais, Warsi, Salman Sagheer, Agha, Mujtaba Hassan, Zahid, Taiba, Becker, Till
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Abingdon Taylor & Francis 03.06.2022 Taylor & Francis Ltd
Témata:	Component and supplier management Decision analysis decision support framework Decision support systems Economic forecasting Environmental impact fuzzy multi-criteria decision making Mathematical analysis Mathematical models Mixed integer multi-objective optimization Multiphase Multiple criterion Multiple objective analysis Nonlinear programming Optimization supplier selection Supply chains Sustainability Sustainable supply chain management Transportation networks Travel time
ISSN:	0305-215X, 1029-0273
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Abstract	With the growing social and environmental concerns, the integration of sustainability in supplier selection and order allocation is of paramount importance. This research presents a holistic multi-phase decision-support framework to solve the sustainable supplier selection and order allocation problem for the multi-echelon supply chain. The framework comprises a multi-objective mixed-integer nonlinear programming mathematical model augmented with fuzzy multi-criteria decision making techniques and forecast demand. The various economic, environmental and social objectives were optimized for a multi-modal transportation network of a multi-echelon supply chain. The results of the mathematical model highlighted the impact of multi-modal transportation on the total cost and total travel time. The results also demonstrated the relationship between the multi-modal transport network and the environmental impact of the supply chain. The proposed multi-phase holistic decision support framework can be used in the comprehensive sustainability-based analysis of supply chains.
AbstractList	With the growing social and environmental concerns, the integration of sustainability in supplier selection and order allocation is of paramount importance. This research presents a holistic multi-phase decision-support framework to solve the sustainable supplier selection and order allocation problem for the multi-echelon supply chain. The framework comprises a multi-objective mixed-integer nonlinear programming mathematical model augmented with fuzzy multi-criteria decision making techniques and forecast demand. The various economic, environmental and social objectives were optimized for a multi-modal transportation network of a multi-echelon supply chain. The results of the mathematical model highlighted the impact of multi-modal transportation on the total cost and total travel time. The results also demonstrated the relationship between the multi-modal transport network and the environmental impact of the supply chain. The proposed multi-phase holistic decision support framework can be used in the comprehensive sustainability-based analysis of supply chains.
Author	Warsi, Salman Sagheer Zahid, Taiba Liaqait, Raja Awais Becker, Till Agha, Mujtaba Hassan
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SubjectTerms	Component and supplier management Decision analysis decision support framework Decision support systems Economic forecasting Environmental impact fuzzy multi-criteria decision making Mathematical analysis Mathematical models Mixed integer multi-objective optimization Multiphase Multiple criterion Multiple objective analysis Nonlinear programming Optimization supplier selection Supply chains Sustainability Sustainable supply chain management Transportation networks Travel time
Title	A multi-criteria decision framework for sustainable supplier selection and order allocation using multi-objective optimization and fuzzy approach
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