A closed-loop supply chain configuration considering environmental impacts: a self-adaptive NSGA-II algorithm

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dc.authoridErfan Babaee Tirkolaee / 0000-0003-1664-9210en_US
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorBabaeinesami, A.
dc.contributor.authorTohidi, H.
dc.contributor.authorGhasemi, P.
dc.contributor.authorGoodarzian, F.
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2022-01-31T07:23:10Z
dc.date.available2022-01-31T07:23:10Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractConfiguration of a supply chain network is a critical issue that contributes to choose the best combination for a set of facilities in order to attain an effective and efficient supply chain management (SCM). Designing a closed-loop distribution network of products is an important field in supply chain network design, which offers a potential factor for reducing costs and improving service quality. In this research, the question concerns a closed-loop supply chain (CLSC) network design considering suppliers, assembly centers, retailers, customers, collection centers, refurbishing centers, disassembly centers and disposal centers. It aims to design a distribution network based on customers’ needs in order to simultaneously minimize the total cost and total CO2 emission. To tackle the complexity of the problem, a self-adaptive non-dominated sorting genetic algorithm II (NSGA-II) algorithm is designed, which is then evaluated against the ?-constraint method. Furthermore, the performance of the algorithm is then enhanced using the Taguchi design method to tune its parameters. The results indicate that the solution time of the self-adaptive NSGA-II approach performs better than the epsilon constraint method. In terms of the self-adaptive NSGA-II algorithm, the average number of Pareto solutions (NPS) for small and medium-sized problems is 6.2 and 11, respectively. The average mean ideal distance (MID) for small and medium-sized problems is 2.54 and 5.01, respectively. Finally, the average maximum spread (MS) for small and medium-sized problems is 3100.19 and 3692.446, respectively. The findings demonstrate that the proposed self-adaptive NSGA-II is capable of generating efficient Pareto solutions. Moreover, according to the results obtained from sensitivity analysis, it is revealed that with increasing the capacity of distribution centers, the amount of shortage of products decreases. Moreover, as the demand increases, the number of established retailers rises. The number of retailers is increasing to some extent to establish 7 retailers. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.en_US
dc.identifier.citationBabaeinesami, A., Tohidi, H., Ghasemi, P., Goodarzian, F., & Tirkolaee, E. B. (2022). A closed-loop supply chain configuration considering environmental impacts: A self-adaptive NSGA-II algorithm. Applied Intelligence, doi:10.1007/s10489-021-02944-9en_US
dc.identifier.doi10.1007/s10489-021-02944-9en_US
dc.identifier.issn0924-669Xen_US
dc.identifier.scopus2-s2.0-85123233208en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.uridoi.org/10.1007/s10489-021-02944-9
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2441
dc.identifier.wosWOS:000744829500001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTirkolaee, Erfan Babaee
dc.language.isoenen_US
dc.publisherSpringer Linken_US
dc.relation.ispartofApplied Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClosed-loop Supply Chain Networken_US
dc.subjectEnvironmental Impactsen_US
dc.subjectMathematical Modelingen_US
dc.subjectSelf-adaptive NSGA-II Algorithmen_US
dc.titleA closed-loop supply chain configuration considering environmental impacts: a self-adaptive NSGA-II algorithmen_US
dc.typeArticleen_US

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