The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a nove...The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode.Spatiotemporal collaboration,along with energy consumption with payload and wind conditions play important roles in delivery route planning.This paper introduces the traveling salesman problem with time window and onboard UAV(TSPTWOUAV)and emphasizes the consideration of real-world scenarios,focusing on time collaboration and energy consumption with wind and payload.To address this,a mixed integer linear programming(MILP)model is formulated to minimize the energy consumption costs of vehicle and UAV.Furthermore,an adaptive large neighborhood search(ALNS)algorithm is applied to identify high-quality solutions efficiently.The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.展开更多
The advancement of self-driving technologies facilitates the emergence of autonomous minibuses(ABs)in public transportation,which could provide flexible,reliable,and safe mobility services.This study develops an AB ro...The advancement of self-driving technologies facilitates the emergence of autonomous minibuses(ABs)in public transportation,which could provide flexible,reliable,and safe mobility services.This study develops an AB routing and scheduling model considering each passenger’s arrival reliability and travel risk.Firstly,to guarantee each passenger’s arrival on time,the arrival reliability(a predetermined threshold of on-time arrival probability ofα=0.9)is included in the constraints.Secondly,three objectives,including system costs,greenhouse gas(GHG)emissions,and travel risk,are optimized in the model.To assess the travel risk of ABs,an enhanced method based on kernel density estimation(KDE)is proposed.Thirdly,an advanced multi-objective adaptive large neighborhood search algorithm(MOALNS)is designed to find the Pareto optimal set.Finally,experiments are conducted in Shanghai to validate model performance.Results show that it can decrease GHG emissions(−2.12%)and risk(−9.47%),while only increasing costs by 2.02%.Furthermore,the proposed arrival reliability constraint can improve an average of 14.70%of passengers to meet their arrival reliability requirement(α=0.9).展开更多
基金Fundamental Research Funds for the Central Universities(2024JBZX038)National Natural Science F oundation of China(62076023)。
文摘The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode.Spatiotemporal collaboration,along with energy consumption with payload and wind conditions play important roles in delivery route planning.This paper introduces the traveling salesman problem with time window and onboard UAV(TSPTWOUAV)and emphasizes the consideration of real-world scenarios,focusing on time collaboration and energy consumption with wind and payload.To address this,a mixed integer linear programming(MILP)model is formulated to minimize the energy consumption costs of vehicle and UAV.Furthermore,an adaptive large neighborhood search(ALNS)algorithm is applied to identify high-quality solutions efficiently.The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.
基金National Natural Science Foundation of China(Grant Nos.71971162 and 52372339).
文摘The advancement of self-driving technologies facilitates the emergence of autonomous minibuses(ABs)in public transportation,which could provide flexible,reliable,and safe mobility services.This study develops an AB routing and scheduling model considering each passenger’s arrival reliability and travel risk.Firstly,to guarantee each passenger’s arrival on time,the arrival reliability(a predetermined threshold of on-time arrival probability ofα=0.9)is included in the constraints.Secondly,three objectives,including system costs,greenhouse gas(GHG)emissions,and travel risk,are optimized in the model.To assess the travel risk of ABs,an enhanced method based on kernel density estimation(KDE)is proposed.Thirdly,an advanced multi-objective adaptive large neighborhood search algorithm(MOALNS)is designed to find the Pareto optimal set.Finally,experiments are conducted in Shanghai to validate model performance.Results show that it can decrease GHG emissions(−2.12%)and risk(−9.47%),while only increasing costs by 2.02%.Furthermore,the proposed arrival reliability constraint can improve an average of 14.70%of passengers to meet their arrival reliability requirement(α=0.9).
