Integrating the Team Orienteering Problem and Occasional Drivers for a Bi-objective Crowdshipping Last-Mile Optimization Model
Crowdshipping and on-demand services are increasingly explored as potential solutions to address last-mile delivery challenges in urban logistics. This research applies Operations Research (OR) techniques to optimize a hybrid crowdshipping system that combines occasional drivers (OD) with traditional delivery services (TD). We propose the Team Orienteering Problem with Occasional Drivers (TOP-OD), a mathematical model and an agile optimization algorithm designed to maximize driver rewards while minimizing delivery costs. Computational experiments show that OD generate higher rewards in dispersed demand scenarios, while the hybrid OD+TD model significantly reduces costs in random customer distributions, however, as demand scales, efficiency gains decline, highlighting trade-offs in urban freight logistics. This study advances combinatorial optimization and heuristic methods in city logistics, providing a decision-support framework for sustainable last-mile delivery.
Keywords: Crowdshipping Urban logistics Last-mile delivery Delivery optimization