基于知识驱动和区间值球面模糊集的基础设施项目可持续供应商分析决策支持架构

《Journal of Industrial Information Integration》：A knowledge-driven decision support architecture for sustainable supplier analysis in an infrastructure project

本文提出了一种知识驱动的决策支持方法（KDDSA），该方法利用基于熵的区间值球面模糊集（IVSFSs）确定标准权重，并引入加权变异系数（WCV）来衡量共识和处理判断中的变异性，从而增强了基础设施项目可持续供应商多准则决策（MCDM）的可靠性。该架构整合了经济、环境、社会和供应商特定等多个维度的评估，为复杂不确定环境下的工业信息集成和供应商管理提供了结构化解决方案。

　　

¹Section snippets

^1.1Literature review

This section reviews the literature in three main areas. Section 2.1 presents fuzzy sets for modeling uncertainty in decision-making. Section 2.2 explores MCDM methods for supplier analysis, while Section 2.3 discusses criteria for supplier analysis. Research gaps and innovations are identified in section 2.4.

^1.2Conceptual framework

Fig. 2 presents a comprehensive knowledge-driven decision support framework for infrastructure projects in uncertain environments. The KDDSA is developed for supplier analysis in an infrastructure project, which involve knowledge-driven evaluation processes, multi-level assessment structures, and divergent opinions regarding indicator assessments. These features are explicitly addressed in the approach through the use of IVSFSs and consensus measurement. As shown in Fig. 2(a), infrastructure

^1.3Background

An infrastructure project involves constructing a ULS (Fig. 6(a)) to enhance logistics efficiency by shifting freight movement underground. A ULS consists of underground tunnels, automated transport mechanisms, and intelligent control systems, facilitating the seamless movement of goods between distribution centers and urban hubs. Supplier analysis plays a crucial role in ensuring the structural integrity, automation, and operational performance of the ULS. The complexity of the ULS requires

^1.4Theoretical implications

This study makes substantial theoretical contributions by advancing MCDM approaches for supplier analysis within the infrastructure and production industries. Traditional methods often struggle to address uncertainties inherent in assessments [12,13,15], particularly when evaluating suppliers across multiple dimensions. In contrast, the application of IVSFSs paired with entropy-based weighting enables a more comprehensive integration of subjective judgments with objective metrics. In addition,

^1.5Conclusions and contributions

Supplier analysis in infrastructure projects requires a structured and multidimensional methodology that simultaneously addresses economic, environmental, social, and supplier-specific considerations. This study introduces a knowledge-driven decision support approach (KDDSA) that integrates interval-valued spherical fuzzy sets (IVSFSs) and entropy-based weighting to enhance the robustness of supplier evaluation. By incorporating both distance and similarity measures, KDDSA provides a