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Title Sustainable resilient recycling partner selection for urban waste management: Consolidating perspectives of decision-makers and experts
ID_Doc 12538
Authors Haseli, G; Torkayesh, AE; Hajiaghaei-Keshteli, M; Venghaus, S
Title Sustainable resilient recycling partner selection for urban waste management: Consolidating perspectives of decision-makers and experts
Year 2023
Published
Abstract In sustainable waste supply chains, selecting recycling partners is an important factor in the decision-making process. Waste supply chains have undergone many fundamental modifications because of the rise of concepts such as sustainability, circular economy, and resilience. To overcome the current shortcomings of the literature on recycling partner selection only based on sustainability aspects, an evaluation framework is developed to address recycling partner selection by considering both sustainability and resilience factors. Although developing a sustainable and resilient evaluation framework improves the process of selecting recycling partners, the problem becomes very complex, and multidimensional decision-makers require reliable and accurate tools to make informed decisions. Multi-criteria decision-making (MCDM) methods are useful decision-making tools with high reliability to address problems under uncertainty. Although previous studies have developed several MCDM methods based on various uncertainty sets, the capability to support efficient and accurate group decision-making by decision-makers' opinions and experts' judgments has been a major disadvantage. Therefore, this study develops a novel decision-making approach using ZE-numbers based on the best-worst method (ZE-BWM) and a combined compromise solution (ZE-CoCoSo). The proposed novel approach for addressing a sustainability and resilience management problem in an urban setting is demonstrated in a real-life case study using Tabriz, Iran as a case study. According to the results, net profit and the robustness of the waste supply chain are the most important criteria.(c) 2023 Elsevier B.V. All rights reserved.
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