Supermarket-chain grocery delivery optimization through crowdshipping

Acknowledging the rising significance of online sales, the grocery business has embraced the challenge of fulfilling the consequently growing consumer expectations for the last-mile delivery efficiency. This paper investigates the grocery delivery optimisation for the supermarket chain based on the crowdshipping mechanism, which can be one of the viable strategies for establishing prompt and affordable delivery service for customers. Considering the deterministic optimisation setting, this study presents a characteristic routing model with crowdsourced couriers named supermarket-chain grocery delivery crowdshipping problem (SCGDCP), which is a variant of the pickup-and-delivery problem, and develops a corresponding mixed integer linear programming (MILP) model. The SCGDCP involves distinctive problem features including individual depots for couriers, multi-trip open routing, and dual time windows of courier operating and order arrival, which pose the computational challenge in problem solving. A bespoke solution procedure based on adaptive variable neighbourhood search (AVNS) strategy is thus designed for tackling the practical-size SCGDCP. The conducted numerical experiments demonstrate the computational efficiency of the proposed MILP model for the small-size instances with no more than 30 grocery orders and the superiority of the developed AVNS procedure for the Grubhub sampling test instances with up to 200 orders.