Optimizing Dynamic and Stochastic Matching in Asset-Sharing Platforms Via Queuing Disciplines and Prioritization Rules

Asset-sharing platforms have become an effective mechanism for enhancing resource utilization across industries representing an innovative application of the sharing economy. These platforms facilitate dynamic matching between supply and demand. Although substantial research has focused on customer-facing models, B2B assetsharing platforms present unique operational challenges arising from resource heterogeneity, stochastic arrivals, and matching mechanisms. This study examines the influence of queue discipline and prioritization rules as strategic design variables in asset-sharing platforms. A discrete-event simulation model is developed to capture the dynamics of the two-sided matching system. The matching logic incorporates multiple queue disciplines and prioritization rules. To identify high-performing platform policies under uncertainty, the model is coupled with a simulation-based optimization framework employing metaheuristic search. Computational results demonstrate that dynamic, priority-based queuing policies consistently outperform simple first-in-first-out or last-in-first-out rules, leading to significant improvements in platform-level profitability. The findings highlight that these policies are critical yet underexplored levers in the operational design of platforms and provide actionable insights for platform operators seeking to improve efficiency in uncertain environments.