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.
Highlights
Cancer has always been a serious threat to human health
Because the Stepwise Weight Assessment Ratio Analysis (SWARA) and Combined Compromise Solution (CoCoSo) methods have their own advantages in determining the weights of criteria and the ranking of alternatives, we extended the SWARA method and improved the CoCoSo method to a probabilistic linguistic environment to form an integrated multiple criteria decision-making (MCDM) model to solve the selection problem of drug cold chain logistics suppliers
Because medicines are the main means to prevent and treat cancer, it is vital to ensure the quality of medicines during transportation and storage, which makes it of great significance for most drug manufacturers to evaluate and select a drug cold chain logistics supplier from the perspective of risk aversion
Summary
Cancer has always been a serious threat to human health. To reduce the incidence of cancer, one important means is to use medicines, such as vaccines, to prevent cancer. Because the SWARA and CoCoSo methods have their own advantages in determining the weights of criteria and the ranking of alternatives, we extended the SWARA method and improved the CoCoSo method to a probabilistic linguistic environment to form an integrated MCDM model to solve the selection problem of drug cold chain logistics suppliers. Analyze the defects of the final aggregation operator in the original CoCoSo method and propose a new integration function to improve the CoCoSo method; Introduce an adjustment coefficient to the SWARA method to make the criteria weights reasonable; Develop an integrated MCDM model based on the combination of the SWARA and CoCoSo methods under the probabilistic linguistic environment; and. The developed integrated MCDM model to select the optimal drug cold chain logistic suppliers for pharmaceutical manufacturing enterprises in China, and highlight the advantage of the PL-CoCoSo method by comparative analysis.
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