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dc.contributor.authorMızrak, Filiz
dc.contributor.authorCantürk, Serkan
dc.date.accessioned2025-10-13T11:55:55Z
dc.date.available2025-10-13T11:55:55Z
dc.date.issued2025en_US
dc.identifier.citationMizrak, A. Prof. F., & Cantürk, A. Prof. S. (2025). Strategic multi-criteria assessment for cold chain logistics optimization in the aviation sector. Research in Transportation Business & Management, 63, 101500. https://doi.org/10.1016/j.rtbm.2025.101500en_US
dc.identifier.issn2210-5395
dc.identifier.urihttps://hdl.handle.net/20.500.12900/733
dc.description.abstractAviation cold chain logistics forms the focus of this study, which introduces a novel hybrid multi-criteria decision-making (MCDM) framework for optimizing sustainable operations, uniquely integrating the newly developed Multi-Objective Seagull-Moth-Salp Swarm Algorithm (MO-SMSA) with K-Means clustering and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). By explicitly addressing the simultaneous demands of sustainability, cost efficiency, operational feasibility, regulatory compliance, and technological integration, the research fills a critical methodological gap in aviation logistics optimization. Qualitative thematic analysis of expert interviews uncovers persistent industry challenges, including the cost-sustainability trade-off, high capital requirements for advanced technology adoption, and regulatory asymmetries across international markets. The methodology applies rigorous data preprocessing and min-max normalization to ensure reproducibility, clusters solutions into efficiency-driven, sustainability-oriented, and technology-enhanced categories, and then employs PROMETHEE to prioritize alternatives, with AI-driven predictive maintenance emerging as the leading solution. The novelty of MO-SMSA lies in its ability to dynamically adapt to shifting decision-maker priorities through scenario analysis and sensitivity testing, capturing complex trade-offs under diverse operational contexts such as high-demand vaccine distribution and general perishable goods transport. Results demonstrate that combining AI, IoT-enabled monitoring, and sustainable packaging yields the most balanced gains in efficiency, environmental performance, and compliance readiness. This study advances the literature by introducing a replicable, practitioner-friendly decision-support model that leverages a cutting-edge optimization algorithm, offering actionable insights for logistics managers, policymakers, and sustainability advocates seeking to strengthen resilience and competitiveness in aviation cold chain operations.en_US
dc.language.isoengen_US
dc.publisherELSEVIERen_US
dc.relation.isversionof10.1016/j.rtbm.2025.101500en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAviation cold chain logisticsen_US
dc.subjectSustainable logistics strategiesen_US
dc.subjectK -means clusteringen_US
dc.subjectPROMETHEEen_US
dc.titleStrategic multi-criteria assessment for cold chain logistics optimization in the aviation sectoren_US
dc.typearticleen_US
dc.departmentİstanbul Atlas Üniversitesien_US
dc.contributor.institutionauthorMızrak, Filiz
dc.identifier.volume63en_US
dc.relation.journalRESEARCH IN TRANSPORTATION BUSINESS AND MANAGEMENTen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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