This study addresses the challenge of effectively and consistently matching technology suppliers and demanders on online trading platforms, where preferences are expressed using vague and linguistically distributed terms. The goal is to develop a matching mechanism that improves satisfaction for both sides, promotes consistency and ensures stable transactions.
A novel framework for calculating the satisfaction of technology trading agents is proposed, which integrates grey relational analysis (GRA) and TOPSIS within the linguistic distribution assessments (LDAs) environment. The proposed approach effectively captures both positional closeness and distribution similarity of LDAs information. Furthermore, to address the objectives of enhancing two-sided satisfaction and minimizing satisfaction deviation, a multi-objective trading matching model is formulated. Subsequently, a consensus-oriented aggregation strategy is developed to transform the model into a single-objective formulation.
Sensitivity analysis confirms the model's robustness and the strategic value of balancing overall satisfaction with consistency. Furthermore, a comparative analysis reveals a critical flaw in traditional models: maximizing only total satisfaction frequently produces unstable, imbalanced matches. In contrast, our proposed framework provides a transparent mechanism for quantifying and optimizing consistency, leading to a significant improvement in the satisfaction balance between matched agents and enabling more robust and equitable collaborations.
This paper marks one of the first explorations of LDAs application in the context of two-sided matching. By integrating GRA and TOPSIS within a fuzzy linguistic context and proposing a novel multi-objective model, it offers a new intelligent optimization paradigm for complex, two-sided matching problems, while expanding grey system theory into the realm of linguistic preference-driven technology transactions.
