应程铄｜北京工业大学｜学者主页

2026

Adaptive bus service scheduling with stochastic running times via an adversarial reinforcement learning approach

Guang-yu Li, Andy H.F. Chow, Cheng-shuo Ying

Transportmetrica B Transport Dynamics

DOI

2026

Risk-aware pedestrian-vehicle interaction: a multi-objective Bayesian-optimised social force model

Yun Ye, Zhihao Zhou, Chengshuo Ying, Jie-Min Xie, X. Chen, Zai-Han Gao

Transportmetrica A: Transport Science

DOI

2026

Corrigendum to “Event-driven dynamic ambulance dispatch: A transformer-based reinforcement learning approach with model explainability” [Trans. Res. Part E: Logist. Trans. Rev., 213 (2026) 104981]

Yi-Mo Yan, Bo Zou, T. Tulabandhula, Lin-Hui Fu, Yong-Hong Kuo, Song-Yi Cui, Chengshuo Ying

Transportation Research Part E: Logistics and Transportation Review

DOI

2026

Event-driven dynamic ambulance dispatch: A transformer-based reinforcement learning approach with model explainability

Yi-Mo Yan, Bo Zou, T. Tulabandhula, Lin-Hui Fu, Yong-Hong Kuo, Song-Yi Cui, Chengshuo Ying

Transportation Research Part E: Logistics and Transportation Review

DOI

2025

Adaptive and flexible rail transit network service dispatching as a partially observable Markov decision process

Shou-yi Wang, Andy H.F. Chow, Cheng-shuo Ying

Transportation Research Part C Emerging Technologies

DOI

2025

Robust optimization for adaptive bus service scheduling with adversarial reinforcement learning under demand uncertainties

Guang-yu Li, Andy H.F. Chow, Cheng-shuo Ying

Transportation Research Part C Emerging Technologies

DOI

2024

Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach

Chengshuo Ying, Andy H. F. Chow, Yimo Yan, Yong-Hong Kuo, Shouyang Wang

Transportation Research Part B-Methodological，Vol. 188

DOI

2023

A policy gradient approach to solving dynamic assignment problem for on-site service delivery

Yimo Yan, Yang Deng, Songyi Cui, Yong-Hong Kuo, Andy H. F. Chow, Chengshuo Ying

Transportation Research Part E-Logistics And Transportation Review，Vol. 178

DOI

2022

Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities

Yimo Yan, Andy H. F. Chow, Chin Pang Ho, Yong-Hong Kuo, Qihao Wu, Chengshuo Ying

Transportation Research Part E-Logistics And Transportation Review，Vol. 162

DOI

2022

Multi-agent deep reinforcement learning for adaptive coordinated metro service operations with flexible train composition

Chengshuo Ying, A. Chow, H. T. Nguyen, K. Chin

Transportation Research Part B: Methodological

DOI

2021

Matching daily home health-care demands with supply in service-sharing platforms

Meiyan Lin, Lijun Ma, Chengshuo Ying

Transportation Research Part E-Logistics And Transportation Review，Vol. 145

DOI

2021

Pareto routing and scheduling of dynamic urban rail transit services with multi-objective cross entropy method

H. M. Nguyen, A. Chow, Chengshuo Ying

Transportation Research Part E: Logistics and Transportation Review

DOI

应程铄

精选作品

期刊论文

Adaptive bus service scheduling with stochastic running times via an adversarial reinforcement learning approach

Risk-aware pedestrian-vehicle interaction: a multi-objective Bayesian-optimised social force model

Corrigendum to “Event-driven dynamic ambulance dispatch: A transformer-based reinforcement learning approach with model explainability” [Trans. Res. Part E: Logist. Trans. Rev., 213 (2026) 104981]

Event-driven dynamic ambulance dispatch: A transformer-based reinforcement learning approach with model explainability

Adaptive and flexible rail transit network service dispatching as a partially observable Markov decision process

Robust optimization for adaptive bus service scheduling with adversarial reinforcement learning under demand uncertainties

Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach

A policy gradient approach to solving dynamic assignment problem for on-site service delivery

Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities

Multi-agent deep reinforcement learning for adaptive coordinated metro service operations with flexible train composition

Matching daily home health-care demands with supply in service-sharing platforms

Pareto routing and scheduling of dynamic urban rail transit services with multi-objective cross entropy method

研究方向