{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T04:40:54Z","timestamp":1773117654749,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Cold vaccine delivery is often known as a high-cost logistic process, which forces many pharmaceutical manufacturers, particularly small- and medium-sized enterprises (SMEs), to subcontract logistics operations of vaccines to third-party logistics (3PL). It is clear that maintaining the traceability and trackability of vaccines in this dynamic collaborative environment is fundamental for guaranteeing the safety of product. However, the lack of a unified vaccine logistics platform holds back comprehensive supervision and traceability, posing significant challenges to the development of useful cold chain logistics systems. To address these challenges, in this study we propose a blockchain-enabled platform for the evaluation and selection of 3PL providers in vaccine supply chains. We leveraged consortium blockchain technology to guarantee data integrity, transparency, and decentralization, facilitating trust among four main players of vaccine supply chain. We utilized smart contracts as a main part of this platform, which are responsible for automating key operational processes, including 3PL evaluation, contract execution, and monitoring. In this respect, the Fuzzy Analytic Hierarchy Process (FAHP) engine is integrated into the proposed platform to enable a data-driven, multi-criteria decision-making framework for selecting the most suitable 3PL providers. We evaluated the proposed platform through case study and gas consumption analysis; the results of the case study validate high operational accuracy (93.21%), precision (90.23%), recall (94.50%), and an F1-score of 92.32% for the platform, which offers a robust solution to enhance accountability, reliability, and decision-making in vaccine distribution networks.<\/jats:p>","DOI":"10.3390\/info17030272","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T15:49:42Z","timestamp":1773071382000},"page":"272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Blockchain-Enabled FAHP-Based Platform for Third-Party Logistics Evaluation and Selection in Cold Vaccine Supply Chains"],"prefix":"10.3390","volume":"17","author":[{"given":"Ali","family":"Barenji","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, New Mexico Tech, Socorro, NM 87801, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"ref_1","unstructured":"WHO (2024, December 31). 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