Designing of an IoT-based sustainable intelligent logistics in healthcare
Journal of Cleaner Production, cilt.573, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 573
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.jclepro.2026.148961
- Dergi Adı: Journal of Cleaner Production
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chimica, Compendex, INSPEC, Public Affairs Index, Academic Search Ultimate (EBSCO), Business Source Ultimate (EBSCO), Engineering Source (EBSCO)
- Anahtar Kelimeler: Intelligent system, IoT, Optimization, Smart city, Sustainable healthcare services
- Ondokuz Mayıs Üniversitesi Adresli: Evet
Özet
Pharmaceutical logistics is responsible for delivering vital medical supplies and medicines from warehouses to pharmacies or healthcare institutions across geographically diverse regions. This study aims to create an efficient route planning, delivery schedule, and optimal charging policy based on Internet of Things (IoT) technologies for the electric vehicle (EV) fleet in the last-mile delivery of the pharmaceutical industry. Therefore, the last-mile sustainable logistics problem is mathematically formulated as a mixed integer programming (MIP). The problem aims to deliver multiple medicines to various geographically scattered locations within the desired time windows with a minimum delay by considering their characteristics and urgencies. Hence, we first employ a spherical fuzzy analytical hierarchy process (SF-AHP) method to obtain the weights and calculate the urgency of medicines. We then develop a heuristic solution approach, namely an adaptive general variable neighborhood search (AGVNS) with several successful integrated mechanisms to solve newly generated problem instances. The comparison of green and conventional diesel fleets, as well as their cost and environmental impacts, are also examined through scenario analyses. Extensive computational results show that the proposed hybrid heuristic approach performs quite well on newly generated instances. Furthermore, the results provide insight into the charging technology usage of the EV fleet and indicate that it has advantages over the conventional fleet in terms of cost and environmental impact when using the presented IoT-based fuzzy weighted optimization framework.