| dc.contributor.author | Mızrak, Filiz | |
| dc.contributor.author | Cantürk, Serkan | |
| dc.date.accessioned | 2025-10-13T11:55:55Z | |
| dc.date.available | 2025-10-13T11:55:55Z | |
| dc.date.issued | 2025 | en_US |
| dc.identifier.citation | Mizrak, 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.2025.101500 | en_US |
| dc.identifier.issn | 2210-5395 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12900/733 | |
| dc.description.abstract | Aviation 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.iso | eng | en_US |
| dc.publisher | ELSEVIER | en_US |
| dc.relation.isversionof | 10.1016/j.rtbm.2025.101500 | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Aviation cold chain logistics | en_US |
| dc.subject | Sustainable logistics strategies | en_US |
| dc.subject | K -means clustering | en_US |
| dc.subject | PROMETHEE | en_US |
| dc.title | Strategic multi-criteria assessment for cold chain logistics optimization in the aviation sector | en_US |
| dc.type | article | en_US |
| dc.department | İstanbul Atlas Üniversitesi | en_US |
| dc.contributor.institutionauthor | Mızrak, Filiz | |
| dc.identifier.volume | 63 | en_US |
| dc.relation.journal | RESEARCH IN TRANSPORTATION BUSINESS AND MANAGEMENT | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |