Navigating the profitability-emissions trade-off in green intermodal transport planning
Journal of the Operational Research Society, 2026 (SCI-Expanded, SSCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1080/01605682.2026.2713708
- Dergi Adı: Journal of the Operational Research Society
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, IBZ Online, Periodicals Index Online, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, zbMATH, Business Source Ultimate (EBSCO), Engineering Source (EBSCO), Health Research Premium Collection (ProQuest), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: bi-objective optimisation, green intermodal transportation, hyper-heuristics, profitability-emissions trade-off, Sustainable logistics
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
This study presents a bi-objective model for intermodal transportation that minimises CO2 emissions while maximising logistics profitability, incorporating government financial incentives. The model is applied to real-world data from an international logistics provider in Turkey’s Aegean region, focusing on demand allocation and transportation mode selection to balance economic and environmental objectives in intermodal transport planning. A Dynamic Epsilon-Greedy Selection-based Hyper-Heuristic (DEG_HH) algorithm is proposed to enhance predefined initial solutions through adaptive heuristic selection. The framework is rigorously evaluated using key performance metrics, demonstrating that DEG_HH outperforms traditional optimisation algorithms in both solution quality and adaptability, making it well-suited for evolving logistics challenges. This study represents the first application of a hyper-heuristic framework in green intermodal transportation planning, providing valuable insights for policymakers and practitioners. It also lays the groundwork for future research on hybrid approaches, machine learning, and real-time data adaptation to enhance the sustainability of global logistics.