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Title: | Cold Chain Logistics Management of Medicine with an Integrated Multi-Criteria Decision-Making Method |
Authors: | Wen, Zhi Liao, Huchang Ren, Ruxue Bai, Chunguang Zavadskas, Edmundas Kazimieras Antuchevičienė, Jurgita Al-Barakati, Abdullah |
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 |
Issue Date: | 2019 |
Publisher: | MDPI |
Citation: | 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. |
Series/Report no.: | 16;23 |
Abstract: | 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. |
Description: | This article belongs to the Special Issue Artificial Intelligence in Health Care |
URI: | http://dspace.vgtu.lt/handle/1/3905 |
ISSN: | 1661-7827 1660-4601 |
Appears in Collections: | Moksliniai straipsniai / Research articles
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