A data-driven compensation scheme for last-mile delivery with crowdsourcing. (February 2023) - Barbosa, Miguel; Pedroso, João Pedro; Viana, Ana
Abstract: 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. Highlights: Data-driven approach to model the occasional couriers' probability of acceptance of a delivery. Dynamic compensation scheme taking that into account. Direct search algorithm for determining the compensation value that minimises the expected cost. Computational analysis of the algorithms proposed.