Flexible supply-demand matching mechanism for C2B crowdsourcing logistics platforms with heterogeneous environment-inclined merchants

Customer-to-Business (C2B) crowdsourcing logistics presents a viable solution to the last-mile delivery problem in e-retailing, offering new prospects for sustainable supply chains. In response to this trend, some digital online platforms have introduced eco-friendly delivery vehicles and launched green delivery services. However, merchants on the demand side often exhibit heterogeneous environmental inclinations, significantly affecting their preferences and choices for delivery vehicle types. To address this, this paper proposes a novel dual-system design comprising two parallel and independent matching systems, aiming to accommodate merchants' diverse preferences through a flexible supply-demand matching mechanism. We subsequently develop a supply allocation optimization model for the platform based on queuing analysis and derive the optimal driver allocation strategy that maximizes platform profitability. A key finding is that market and individual environmental awareness exert opposite effects on the platform's optimal allocation decision. Our proposed dual-system design achieves a harmonious balance between reducing carbon emissions and enhancing profitability, resulting in a 13.3% reduction in platform carbon emissions and a 17.7% increase in profits in the baseline scenario. Interestingly, government environmental propaganda may not necessarily contribute to reducing platform carbon emissions unless complementary measures are implemented to control excessive environmental premiums associated with green delivery vehicles. Our study provides valuable insights for promoting the behavioral operations management of crowdsourcing logistics platforms.