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Open AccessArticle

Cold Chain Logistics Management of Medicine with an Integrated Multi-Criteria Decision-Making Method

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Business School, Sichuan University, Chengdu 610064, China
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Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain
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School of Management and Economics, University of Electronic Science and Technology of China, Chengdu 610054, China
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Institute of Sustainable Construction, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania
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Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania
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Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(23), 4843; https://doi.org/10.3390/ijerph16234843
Received: 25 October 2019 / Revised: 25 November 2019 / Accepted: 27 November 2019 / Published: 2 December 2019
(This article belongs to the Special Issue Artificial Intelligence in Health Care)
Medicine is the main means to reduce cancer mortality. However, some medicines face various risks during transportation and storage due to the particularity of medicines, which must be kept at a low temperature to ensure their quality. In this regard, it is of great significance to evaluate and select drug cold chain logistics suppliers from different perspectives to ensure the quality of medicines and reduce the risks of transportation and storage. To solve such a multiple criteria decision-making (MCDM) problem, this paper proposes an integrated model based on the combination of the SWARA (stepwise weight assessment ratio analysis) and CoCoSo (combined compromise solution) methods under the probabilistic linguistic environment. An adjustment coefficient is introduced to the SWARA method to derive criteria weights, and an improved CoCoSo method is proposed to determine the ranking of alternatives. The two methods are extended to the probabilistic linguistic environment to enhance the applicability of the two methods. A case study on the selection of drug cold chain logistics suppliers is presented to demonstrate the applicability of the proposed integrated MCDM model. The advantages of the proposed methods are highlighted through comparative analyses. View Full-Text
Keywords: clinical decision-support systems; multiple criteria decision-making; probabilistic linguistic term set; stepwise weight assessment ratio analysis (SWARA); combined compromise solution (CoCoSo); drug cold chain logistics clinical decision-support systems; multiple criteria decision-making; probabilistic linguistic term set; stepwise weight assessment ratio analysis (SWARA); combined compromise solution (CoCoSo); drug cold chain logistics
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Wen, Z.; Liao, H.; Ren, R.; Bai, C.; Zavadskas, E.K.; Antucheviciene, J.; Al-Barakati, A. Cold Chain Logistics Management of Medicine with an Integrated Multi-Criteria Decision-Making Method. Int. J. Environ. Res. Public Health 2019, 16, 4843.

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