Environmental sustainability and risk-aware optimization in hybrid truck-drone logistics: A holistic multi-objective framework

Compared to conventional truck-only systems, hybrid truck–drone delivery systems offer transformative potential for last-mile logistics by addressing operational inefficiencies, minimizing environmental impact, and enhancing safety through risk-aware optimization. This study develops a stochastic multi-objective optimization framework grounded in the Specific Operations Risk Assessment (SORA) methodology. By extending the vehicle routing problem with pickup and delivery (VRPPD) and the flying sidekick traveling salesman problem (FSTSP), the model incorporates battery optimization, CO 2 emissions reduction, and energy-efficient routing strategies. Delivery cost, time, energy consumption, operational risk, and battery performance are all optimized using a mixed-integer linear programming (MILP) and non-dominated sorting genetic algorithm II (NSGA-II) technique. Sensitivity analysis show that increasing drone fleet size and efficiency results in significant cost, time, and energy savings while improving safety. The model's flexibility in both urban and remote delivery contexts is confirmed by numerical trials. In line with life cycle analysis (LCA), this study offers practical advice for environmentally responsible and risk-aware logistics, assisting decision-makers and industry participants in the development of scalable and sustainable solutions. • Optimizes cost, time, risk, and energy in hybrid truck–drone logistics. • Uses MILP and NSGA-II for multi-objective routing and energy savings. • Incorporates SORA for risk-aware UAV operations in urban logistics. • Sensitivity shows drone efficiency cuts cost, risk, and CO 2 emissions. • Achieves energy-efficient delivery via battery-aware UAV coordination.