A machine learning-based two-stage approach for the location of undesirable facilities in the biomass-to-bioenergy supply chain

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dc.authoridBilgen, Bilge/0000-0002-2361-0413
dc.authoridOzsoydan, Fehmi Burcin/0000-0002-6368-4425
dc.authoridYunusoglu, Pinar/0000-0001-8026-4935
dc.contributor.authorYunusoglu, Pinar
dc.contributor.authorOzsoydan, Fehmi Burcin
dc.contributor.authorBilgen, Bilge
dc.date.accessioned2025-03-20T09:51:13Z
dc.date.available2025-03-20T09:51:13Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractBiomass-to-bioenergy supply chain management is an integral part of the sustainable industrialization of energy conversion through biomass to bioenergy by managing economic, environmental, and social challenges encountered in each supply chain stage. Motivated by a real-world biomass-to-bioenergy supply chain network design (BSCND) problem, this study addresses the location of undesirable facilities for the first time in the BSCND literature. The motivation of this study is to develop a machine learning-based two-stage approach for solving the BSCND problem with undesirable facilities that have a negative impact on surrounding communities. The first stage employs the k-means clustering algorithm to alleviate the complexity of the problem, and the second stage utilizes a novel pre-emptive goal programming (PGP) approach to optimize two distinct objectives hierarchically. The first objective maximizes the sum of the distances between all clients and the open facilities, which is the well-known objective of the obnoxious p-median (OpM) problem. The second objective maximizes the total profit of the entire supply chain. The applicability of the proposed solution approach is shown through the case problem, and performance of the two-stage approach is validated using randomly generated test problems. The computational results indicate the effectiveness of the clustering methodology in reducing the complexity of the problem while the PGP achieves the optimal configuration of the biomass-to-bioenergy supply chain handling the hierarchical objectives. The optimal solution of the case problem was achieved within 25,239.36 s execution time, and the total profit of the supply chain is $6,776,870.22 with 735 km total distance to clients. The average optimality gap for the first phase of the PGP is 4.97%, and the average optimality gap for the second phase of the PGP is 0.01% for the generated test problems.
dc.identifier.doi10.1016/j.apenergy.2024.122961
dc.identifier.issn0306-2619
dc.identifier.issn1872-9118
dc.identifier.scopus2-s2.0-85187196660
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.apenergy.2024.122961
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2472
dc.identifier.volume362
dc.identifier.wosWOS:001210052100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofApplied Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250319
dc.subjectBiomass-to-bioenergy supply chain
dc.subjectPre-emptive goal programming
dc.subjectObnoxious p -median model
dc.subjectMachine learning
dc.subjectK-means clustering
dc.titleA machine learning-based two-stage approach for the location of undesirable facilities in the biomass-to-bioenergy supply chain
dc.typeArticle

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