TY  - JOUR
AU  - Haseli, Gholamreza
AU  - Torkayesh, Ali Ebadi
AU  - Hajiaghaei-Keshteli, Mostafa
AU  - Venghaus, Sandra
TI  - Sustainable resilient recycling partner selection for urban waste management: Consolidating perspectives of decision-makers and experts
JO  - Applied soft computing
VL  - 137
SN  - 1568-4946
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - FZJ-2023-01588
SP  - 110120 -
PY  - 2023
AB  - 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 Z-numbers based on the best-worst method (Z-BWM) and a combined compromise solution (Z-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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000995927400001
DO  - DOI:10.1016/j.asoc.2023.110120
UR  - https://juser.fz-juelich.de/record/1005657
ER  -