Fairness in online vehicle-cargo matching: An intuitionistic fuzzy set theory and tripartite evolutionary game approach
Binzhou Yang, Ke Han, Wenrui Tu, Qian Ge

TL;DR
This paper introduces a novel fairness-aware vehicle-cargo matching model using intuitionistic fuzzy set theory and a tripartite evolutionary game, improving stakeholder satisfaction and platform revenue in online freight matching.
Contribution
It develops a new fairness-oriented optimization model with an adaptive algorithm and a tripartite game framework, integrating fuzzy set theory and game theory for online vehicle-cargo matching.
Findings
Enhanced stakeholder satisfaction with fairness considerations
Positive revenue growth for the platform
Better off for shippers, carriers, and the platform
Abstract
This paper explores the concept of fairness and equitable matching in an on-line vehicle-cargo matching setting, addressing the varying degrees of satisfaction experienced by shippers and carriers. Relevant indicators for shippers and carriers in the on-line matching process are categorized as attributes, expectations, and reliability, which are subsequent quantified to form satisfaction indicators. Employing the intuitionistic fuzzy set theory, we devise a transformed vehicle-cargo matching optimization model by combining the fuzzy set's membership, non-membership, and uncertainty information. Through an adaptive interactive algorithm, the matching scheme with fairness concerns is solved using CPLEX. The effectiveness of the proposed matching mechanism in securing high levels of satisfaction is established by comparison with three benchmark methods. To further investigate the impact of…
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Taxonomy
TopicsUrban and Freight Transport Logistics · Transportation and Mobility Innovations · Vehicle Routing Optimization Methods
