Pricing and matching for on-demand platform considering customer queuing and order cancellation

Abstract Queuing on an on-demand platform may make some customers disgust and give up using it, and the customers who have confirmed the orders may also cancel the orders due to some uncertain factors, which causes certain opportunity loss to the platform. This article considers both customer queuing and order cancellation (COC) behaviour, and studies optimal pricing and matching of the profit-maximizing platform. We first construct models without and with COC behaviour (cases N and C), and then propose two strategies of the platform to deal with COC behaviour, including the penalty strategy (case PC) and the penalty-subsidy strategy (case PSC). By solving these models and analysing, we find that although the penalty strategy intuitively discourages some customers from using on-demand services, the platform reduces the service price because of penalty fee, which indirectly encourages more customers who may not cancel orders to request services. We also find that when the COCR is greater than a certain critical point, both the penalty strategy and penalty-subsidy strategy are advantageous, while the penalty strategy is the best. However, when the COCR is less than the critical point, the penalty strategy is unfavourable, while the penalty-subsidy strategy is advantageous. Abbreviations: COC: customer queuing and order cancellation; COCR: customer order cancellation rate.