A sustainability-aware optimization framework for port-hinterland truck scheduling

Abstract Ports are critical gateways for trade and regional growth but also generate substantial externalities such as emissions, congestion, and safety risks. Growing maritime transport and port throughput intensify long-haul diesel truck traffic on hinterland access roads, causing peak-hour bottlenecks, long queues, and higher environmental impacts. Despite many proposed operational strategies, these challenges remain insufficiently addressed. This paper develops a mixed-integer linear programming (MILP) model for sustainable port-hinterland truck scheduling within an integrated chassis-based remote gate (CRG) and battery-electric trucks (BET) framework for short-haul transport. Using a major UK port case study with real traffic data, the model minimizes total logistics costs, including fixed investments, transport, waiting and idling times for short- and long-haul trucks, and $$\hbox {CO}_2$$ CO 2 emissions. To handle large-scale instances, a two-stage adaptive genetic algorithm (TS-AGA) is proposed and benchmarked against the MILP and a two-stage simulated annealing approach. Collaborative and non-collaborative scheduling strategies are compared to assess the benefits of coordinated decision-making. Results show that collaborative scheduling reduces total system costs by about 13.6% by better synchronizing long-haul diesel truck arrivals with short-haul battery-electric truck operations. Sensitivity analysis further examines how long-haul travel-time uncertainty affects congestion and economic performance. Overall, the proposed MILP model and TS-AGA solution approach demonstrate that coordinated truck scheduling can substantially improve the operational efficiency and sustainability of CRG-BET-based port-hinterland systems. By jointly optimizing long-haul and short-haul operations under collaborative and uncertainty-aware strategies, the study offers a practical and scalable scheduling methodology to reduce logistics costs, congestion, and emissions while supporting sustainable port-city connectivity.