Purpose

The purpose of this study is to establish a synthetic group decision framework based on the Pythagorean fuzzy (PF) set to select the optimal medicine cold chain logistics provider (MCCLP). Fierce market competition makes enterprises must constantly improve every link in the process of enterprise sustainable development. The evaluation of MCCLP in pharmaceutical enterprises is an important link to enhance the comprehensive competitiveness. Because of the fuzziness of expert cognition and the complexity of the decision procedure, PF set can effectively handle the uncertainty and ambiguity in the process of multi-criteria group decision decision-making (MCGDM).

Design/methodology/approach

This paper develops an integrated group decision framework through combining the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique and combined compromise solution (CoCoSo) approach to select a satisfactory MCCLP within PF circumstances. First, the PF set is used to process the ambiguity and uncertainty of the cognition ability of experts. Second, a novel PF knowledge measure is propounded to measure the vagueness of the PF set. Third, a comprehensive criterion weight determination technique is developed through aggregating subjective weights attained utilizing the PF DEMATEL approach and objective weight deduced by knowledge measure method. Furthermore, an integrated MCGDM approach based on synthetic weight and CoCoSo method is constructed.

Findings

The outcomes of sensibility analysis and comparison investigation show that the suggested decision framework can help decision experts to choose a satisfactory MCCLP scientifically and reasonably. Accordingly, the propounded comprehensive decision framework can be recommended to enterprises and organizations to assess the MCCLP for their improvement of core competitiveness.

Originality/value

MCCLP selection is not only momentous for pharmaceutical enterprises to improve transportation quality and ensure medicine safety but also provides a strong guarantee for enterprises to improve their core competitiveness. Nevertheless, enterprises face certain challenges due to the uncertainty of the assessment environment as well as human cognition in the process of choosing a satisfactory MCCLP. PF set possesses a formidable capability to address the uncertainty and imprecision information in the process of MCGDM. Therefore, pharmaceutical enterprises can implement the proposed method to evaluate the suppliers to further improve the comprehensive profit of enterprises.

中文翻译：

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利用毕达哥拉斯模糊 DEMATEL-CoCoSo 方法进行医药冷链物流供应商选择的集成群体决策支持框架

 目的

本研究的目的是建立一个基于毕达哥拉斯模糊 （PF） 集的综合群体决策框架，以选择最优的医药冷链物流提供商 （MCCLP）。激烈的市场竞争使得企业在企业可持续发展过程中必须不断改进每一个环节。制药企业MCCLP评价是提升企业综合竞争力的重要环节。由于专家认知的模糊性和决策过程的复杂性，PF 集可以有效处理多准则群体决策 （MCGDM） 过程中的不确定性和模糊性。

设计/方法/方法

本文通过结合决策试验和评估实验室 （DEMATEL） 技术和组合折衷解决方案 （CoCoSo） 方法，开发了一个综合的群体决策框架，以在 PF 情况下选择令人满意的 MCCLP。首先，使用 PF 集处理专家认知能力的模糊性和不确定性;其次，提出了一种新的 PF 知识度量来测量 PF 集的模糊性。第三，通过聚合使用 PF DEMATEL 方法获得的主观权重和通过知识测量法推导出的客观权重，开发了一种全面的标准权重确定技术。此外，构建了一种基于合成重量和 CoCoSo 方法的集成 MCGDM 方法。

 发现

敏感性分析和比较调查结果表明，建议的决策框架可以帮助决策专家科学合理地选择满意的MCCLP。因此，所提出的综合决策框架可以推荐给企业和组织，以评估MCCLP对他们提高核心竞争力的作用。

 原创性/价值

MCCLP的选择不仅对制药企业提高运输质量、保障药品安全具有重大意义，也为企业提高核心竞争力提供了有力保障。然而，由于评估环境的不确定性以及人类认知的不确定性，企业在选择满意的MCCLP的过程中面临着一定的挑战。PF 集具有强大的能力，可以解决 MCGDM 过程中的不确定性和不精确信息。因此，制药企业可以实施所提出的方法对供应商进行评估，以进一步提高企业的综合利润。