Incentive-compatible auction mechanisms for crowdshipping: Modeling, solution approach, and systemic impacts

Crowdshipping offers advantages in terms of cost reduction and operational scalability. However, the uncertain availability and strategic behavior of crowdsourced couriers pose significant challenges to reliable and efficient task allocation. In particular, crowdsourced couriers may inflate their reported detour costs to obtain higher compensation, thereby undermining system efficiency. This study investigates the integrated task allocation and pricing problem in a hybrid crowdshipping system that combines crowdsourced and full-time couriers. Unlike standard approaches that rely on computationally intensive VCG implementation, we build a framework on the Leonard mechanism. Through a dual analysis of the allocation problem, we characterize the minimal competitive equilibrium and derive an incentive-compatible pricing scheme. To support real-world implementation, we propose a self-adaptive auction mechanism. This mechanism ensures incentive-compatible bidding and polynomial-time solvability by integrating ascending-price (modified proxy-DGS) and descending-price (modified proxy-LVD) strategies. Beyond mechanism design, we assess the systemic impacts of crowdshipping on traffic congestion and emissions. Analytical and numerical evaluations identify threshold conditions under which crowdshipping enhances both traffic and environmental performance. This research contributes a novel mechanism design framework for hybrid logistics platforms and provides policy-relevant insights into the operational and environmental implications of crowdshipping systems.