Freight distribution optimization by implementing the team orienteering problem with occasional drivers

Abstract This research evaluates the effectiveness of a hybrid crowdshipping system that integrates occasional drivers with traditional delivery services (OD+TD) to improve the efficiency of freight distribution. By implementing the team orienteering problem with occasional drivers model, we develop a mathematical model and an agile optimization algorithm focused primarily on maximizing rewards for drivers, with a secondary objective of minimizing delivery costs across various customer configurations. Our computational experiments demonstrate that the agile algorithm consistently delivers high‐quality solutions in reasonable computational times. The analysis reveals that occasional drivers generate higher rewards in geographically dispersed scenarios compared to clustered distributions, where rewards are comparatively lower. Additionally, the OD + TD system significantly reduces delivery costs, particularly in scenarios with randomly distributed customers. However, as the customer base expands, the relative benefits of the hybrid system tend to decrease, revealing a complex relationship between demand size and delivery efficiency. This research contributes valuable insights into the dynamics of hybrid distribution systems and establishes a framework for further exploration of optimization strategies in logistics.