A data-driven compensation scheme for last-mile delivery with crowdsourcing

A recent relevant innovation in last-mile delivery is to consider the possibility of goods being delivered by couriers appointed through crowdsourcing. In this paper we focus on the setting of in-store customers delivering goods, ordered by online customers, on their way home. We assume that not all the proposed delivery tasks will necessarily be accepted, and use logistic regression to model the crowd agents' willingness to undertake a delivery. This model is then used to build a novel compensation scheme that determines reward values, based on the current plan for the professional fleet's routes and on the couriers' probabilities of acceptance, by employing a direct search algorithm that seeks to minimise the expected cost.

Automated summary

A recent innovation in last-mile delivery is considering the possibility of using crowdsourced couriers for delivering goods. This paper focuses on in-store customers delivering goods ordered by online customers on their way home. Logistic regression is used to model the willingness of crowd agents to undertake a delivery, and a compensation scheme is developed based on the current plan for the professional fleet's routes and the couriers' probabilities of acceptance, using a direct search algorithm to minimize expected cost.