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Title A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network
ID_Doc 73186
Authors Nayeri, S; Torabi, SA; Tavakoli, M; Sazvar, Z
Title A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network
Year 2021
Published
DOI 10.1016/j.jclepro.2021.127691
Abstract This study proposes a multi-objective mixed-integer programming model to configure a sustainable supply chain network while considering resilience and responsiveness measures. The model aims at minimizing the total costs and environmental damages while maximizing the social impacts, as well as the responsiveness and resilience levels of the supply chain network. An improved version of the fuzzy robust stochastic optimization approach is proposed to tackle the uncertain data arising in the dynamic business environment. Furthermore, a new version of meta-goal programming named the multi-choice meta-goal programming associated with a utility function is developed to solve the resulting multi-objective model. A case study in the water heater industry is investigated to illustrate the application of the proposed model and its solution approach. The numerical results validate the proposed model and the developed solution method. Finally, interactions between the sustainability, responsiveness, and resilience dimensions are investigated and several sensitivity analyses are performed on critical parameters by which useful managerial insights are provided.
Author Keywords Supply chain network design; Sustainability; Resilience; Responsiveness; Fuzzy robust stochastic optimization; Multi-choice meta-goal programming
Index Keywords Index Keywords
Document Type Other
Open Access Open Access
Source Science Citation Index Expanded (SCI-EXPANDED)
EID WOS:000668106900001
WoS Category Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences
Research Area Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology
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