Green vehicle technology to enhance the performance of a European port: a simulation model with a cost-benefit approachTri Thanh- Le
Shayan Kavakebaa,Trung Thanh Nguyena , Kay McGinleyb, Zaili Yanga, Ian Jenkinsona, Roisin Murrayb
a Liverpool Logistics, Offshore and Marine Research Institute, School of Engineering, Technology and Maritime Operations, Liverpool John Moores University, Liverpool L3 3AF, UK
b Department of Transport Engineering, Dublin Institute of Technology, Bolton St., Dublin 1, Ireland
Published in: Transportation Research, Part C: Emerging Technologies, 60. pp. 169-188. ISSN 1879-2359 (ranked 5th in Transportation Science & Technology)
In this paper, we study the impact of using a new intelligent vehicle technology on the performance and total cost of a European port, in comparison with existing vehicle systems like trucks. Intelligent autonomous vehicles (IAVs) are a new type of automated guided vehicles (AGVs) with better maneuverability and a special ability to pick up/drop off containers by themselves. To identify the most economical fleet size for each type of vehicle to satisfy the port’s performance target, and also to compare their impact on the performance/cost of container terminals, we developed a discrete-event simulation model to simulate all port activities in micro-level (low-level) details. We also developed a cost model to investigate the present values of using two types of vehicle, given the identified fleet size. Results of using the different types of vehicles are then compared based on the given performance measures such as the quay crane net moves per hour and average total discharging/loading time at berth. Besides successfully identifying the optimal fleet size for each type of vehicle, simulation results reveal two findings: first, even when not utilising their ability to pick up/drop off containers, the IAVs still have similar efficacy to regular trucks thanks to their better maneuverability. Second, enabling IAVs’ ability to pick up/drop off containers significantly improves the port performance. Given the best configuration and fleet size as identified by the simulation, we use the developed cost model to estimate the total cost needed for each type of vehicle to meet the performance target. Finally, we study the performance of the case study port with advanced real-time vehicle dispatching/scheduling and container placement strategies. This study reveals that the case study port can greatly benefit from upgrading its current vehicle dispatching/scheduling strategy to a more advanced one.
Discrete-event simulation, Fleet sizing, Intelligent autonomous vehicles, Automated guided vehicles, Container terminals, Cost-benefit analysis