Data-driven ambiguous cognitive map for complex decision-making in supply chain management

Abstract

Fuzzy cognitive maps (FCMs) have the potential to model complex systems, but they face challenges in uncertainty, complexity, and dynamic conditions. This study tackles three main issues: modeling and quantifying uncertainty in relationships and weights with imprecise inputs, managing the complexity as the number of activation levels and causal relationships increases, and determining appropriate weights and thresholds in uncertain contexts. By using ambiguous set theory, the research introduces the ambiguous cognitive map (ACM) to improve the traditional FCM and address these problems. This theory allows for the representation of states with four membership values: true, false, partially true, and partially false, which provides a more refined approach to managing uncertainty. Mathematical formulas are employed by ACM to calculate weights based on these membership values instead of randomly selecting. The introduction of rank allows for the identification of the most influential state by its highest rank in priority decisions. The application of ACM in decision-making scenarios related to the supply chain system demonstrates its efficiency in systematically prioritizing and resolving complex decisions. The ACM effectively identifies key variables and provides actionable rankings to support decision-making in the supply chain system. The results demonstrate that ACM offers a systematic approach to resolving complex decisions under uncertainty.

Impact Statement ACMs replace the conventional random assignment of relationship weights with a mathematical formulation based on the four membership values, enhancing the accuracy and reliability of the modeled system. The study also introduces a rank-based decision-making process, where the most influential state is determined by the highest rank derived from the membership values. The proposed ACM framework not only addresses the limitations of traditional FCMs but also opens new avenues for artificial intelligence (AI)-driven analysis of complex, uncertain systems.