Associate Prof. Filiz Mizrak , Assistant Prof. Serkan Cantürk
{"title":"航空领域冷链物流优化的多准则战略评价","authors":"Associate Prof. Filiz Mizrak , Assistant Prof. Serkan Cantürk","doi":"10.1016/j.rtbm.2025.101500","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47453,"journal":{"name":"Research in Transportation Business and Management","volume":"63 ","pages":"Article 101500"},"PeriodicalIF":4.4000,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Strategic multi-criteria assessment for cold chain logistics optimization in the aviation sector\",\"authors\":\"Associate Prof. Filiz Mizrak , Assistant Prof. Serkan Cantürk\",\"doi\":\"10.1016/j.rtbm.2025.101500\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":47453,\"journal\":{\"name\":\"Research in Transportation Business and Management\",\"volume\":\"63 \",\"pages\":\"Article 101500\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research in Transportation Business and Management\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210539525002159\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in Transportation Business and Management","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210539525002159","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
Strategic multi-criteria assessment for cold chain logistics optimization in the aviation sector
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.
期刊介绍:
Research in Transportation Business & Management (RTBM) will publish research on international aspects of transport management such as business strategy, communication, sustainability, finance, human resource management, law, logistics, marketing, franchising, privatisation and commercialisation. Research in Transportation Business & Management welcomes proposals for themed volumes from scholars in management, in relation to all modes of transport. Issues should be cross-disciplinary for one mode or single-disciplinary for all modes. We are keen to receive proposals that combine and integrate theories and concepts that are taken from or can be traced to origins in different disciplines or lessons learned from different modes and approaches to the topic. By facilitating the development of interdisciplinary or intermodal concepts, theories and ideas, and by synthesizing these for the journal''s audience, we seek to contribute to both scholarly advancement of knowledge and the state of managerial practice. Potential volume themes include: -Sustainability and Transportation Management- Transport Management and the Reduction of Transport''s Carbon Footprint- Marketing Transport/Branding Transportation- Benchmarking, Performance Measurement and Best Practices in Transport Operations- Franchising, Concessions and Alternate Governance Mechanisms for Transport Organisations- Logistics and the Integration of Transportation into Freight Supply Chains- Risk Management (or Asset Management or Transportation Finance or ...): Lessons from Multiple Modes- Engaging the Stakeholder in Transportation Governance- Reliability in the Freight Sector