智能网联环境下机场摆渡车调度优化研究OA

Currently,research on airport special vehicle scheduling primarily focuses on minimizing the number of vehicles,total travel distance,and task load discrepancies,while neglecting passengers'travel experience.Additionally,scheduling still relies on manual methods and lacks unified support from intelligent sensing and communication systems.To address this,we develop a framework for airport special vehicle scheduling in an intelligent connected environment,including scheduling models and algorithms.Based on prospect theory and fuzzy membership functions,a passenger satisfaction function is established,and an improved shuttle bus scheduling model is developed.The model aims to minimize vehicle usage,minimize the discrepancy in the number of flights served by each vehicle,and maximize passenger satisfaction.The NSGA-II algorithm is used to solve the model,with instance validation based on flight data from Nanjing Lukou Airport.The results show that compared to the First-Come-First-Serve(FCFS)algorithm and the GA Improve algorithm,although the NGSA-II algorithm does not reduce the number of vehicles used,it achieved markedly better workload balance,cutting variance by107.4 relative to FCFS and by1.1 relative to GA Improve,while increasing passenger satisfaction by 33.5％over FCFS and 12.4％over GA Improve,thereby providing effective decision support for intelligent airport operations.