Rolling optimal scheduling for urban parcel crowdsourced delivery with new order insertion

Abstract

The rapid development of mobile information technology has introduced numerous new solutions for delivery companies to enhance profits. One such solution employed by some companies is crowdsourced delivery. In this paper, we focus on rolling optimal scheduling for urban parcel crowdsourced delivery by utilizing private cars that will be in passing with the incorporation of new order insertion. The bonus incentive strategy is introduced to enhance the delivery probability of private car drivers. A static model and a rolling optimization model to maximize profits and the number of parcels delivered by private cars are established. To address the NP-hard problem, a hybrid genetic algorithm and insertion algorithm are designed. Numerical experiments are carried out to verify the proposed method in different scenarios, including the scattered network, clustered network, Dalian network, and Foursquare network. The computational results demonstrate that the method enhances the matching ratio and increases profits. Utilizing private cars that will be in passing for urban parcel delivery reduces the need for dedicated vehicles, mitigating traffic growth and alleviating traffic congestion. Increasing the private car-parcel ratio improves profits and the matching ratio while reducing traffic growth. Raising the incentive coefficient for bonuses increases the matching ratio and detour distance, but profits first increase and then decrease, and delivery distance by dedicated vehicles decreases. Our research findings offer a more rational basis for green urban parcel delivery decision-making by companies.