Operational Cost Optimization of Delivery Fleets Consisting of Mobile Robots and Electric Trucks

Rising demand for last-mile deliveries in the logistics sector has prompted the adoption of Autonomous Delivery Robots (ADRs) and electric trucks (eTrucks) for their efficiency and cost-effectiveness. This paper proposes an optimization model for an integrated eTruck-and-ADR system. The model employs a range of information sources to optimize vehicle routing and robot allocation, emphasizing energy efficiency and operating cost. This includes incorporating Geographic Information System (GIS) to estimate customer demand based on demographics and utilizing a battery aging/degradation model to account for hardware depreciation. A metaheuristic Genetic Algorithm is employed to solve optimal vehicle routing and customer node clustering. In a simulated case study conducted with real GIS and geographic data, the proposed model demonstrates efficacy in determining the optimal number of ADRs for specific census tracts, with a cost breakdown highlighting the dominance of human labor costs.