Analysis of in-store crowdshipping in a stochastic dynamic pickup-and-delivery system

To meet the increasing demands of home delivery resulting from the proliferation of internet shopping and compounded by the rising expectation of fast fulfillment (often within hours of request), companies seek new delivery methods supported by information and communication technologies. In this study, we consider a dispatching platform with delivery capacity consisting of a dedicated fleet of vehicles complemented by crowdsourced couriers. We consider the crowdsourced couriers to be in-store customers who, upon checking out of the store, declare themselves available to deliver one or more requests from e-shoppers. The role of the collaborative platform is to aggregate e-shopper orders from the participating businesses and then manage the routing for the pickup of the corresponding products at the physical stores and the subsequent deliveries to the e-shoppers’ locations. We model this dynamic stochastic pickup-and-delivery problem as a Markov decision process to represent the uncertainty in the e-shopper requests and in-store crowdshipper appearances. We adapt a real-time insertion method enhanced with a cost function approximation to account for differences in the temporal availability of the dedicated vehicles and in-store crowdshippers. We conduct computational experiments to demonstrate the conditions under which in-store crowdshippers provide a cost benefit.