代表性研究成果
l Ying Cheng-Shuo, Chow Andy H.F.*, Yan Yi-Mo, Kuo Yong-Hong, Wang Shou-Yang. Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach[J]. Transportation Research Part B: Methodological, 2024, 188: 103067. (中科院1区, AJG 4星)
l Ying Cheng-Shuo, Chow Andy H.F.*, Nguyen Hoa T.M., Chin Kwai-Sang. Multi-agent deep reinforcement learning for adaptive coordinated metro service operations with flexible train composition[J]. Transportation Research Part B: Methodological 2022, 161: 36–59. (中科院1区, AJG 4星)
l Ying Cheng-Shuo, Chow Andy H.F.*, Wang Yi-Hui, Chin Kwai-Sang. Adaptive metro service schedule and train composition with a proximal policy optimization approach based on deep reinforcement learning[J]. IEEE Transactions on Intelligent Transportation Systems 2021, 23(7): 6895–6906. (中科院1区)
l Ying Cheng-Shuo, Chow Andy H.F.*, Chin Kwai-Sang. An actor-critic deep reinforcement learning approach for metro train scheduling with rolling stock circulation under stochastic demand[J]. Transportation Research Part B: Methodological 2020, 140: 210–235. (中科院1区, AJG 4星)
l Li Yan-Lai, Ying Cheng-Shuo*, Chin Kwai-Sang, Yang Hong-Tai, Xu Jie*. Third-party reverse logistics provider selection approach based on hybrids-information MCDM and cumulative prospect theory[J]. Journal of Cleaner Production 2018, 195: 573–584. (中科院1区, AJG 2星)
l Ying Cheng-Shuo, Li Yan-Lai*, Chin Kwai-Sang, Yang Hong-Tai, Xu Jie*. A new product development concept selection approach based on cumulative prospect theory and hybrid-information MADM[J]. Computers & Industrial Engineering 2018, 122: 251–261. (中科院2区, AJG 2星)
l Wang Shou Yi, Chow Andy H F, Ying Cheng-Shuo. Adaptive and flexible rail transit network service dispatching as a partially observable markov decision process[J]. Transportation Research Part C: Emerging Technologies, 2025, 179: 105286. (中科院1区)
l Li Guang Yu, Chow Andy H F, Ying Cheng-Shuo. Robust optimization for adaptive bus service scheduling with adversarial reinforcement learning under demand uncertainties[J]. Transportation Research Part C: Emerging Technologies, 2025, 178: 105222. (中科院1区)
l Deng Yang, Yan Yimo, Chow Andy H F, Zhou Zhili, Ying Cheng-Shuo, Kuo Yong Hong. A proximal policy optimization approach for food delivery problem with reassignment due to order cancellation[J]. Expert Systems with Applications, 2024, 258: 125045. (中科院1区, AJG 1星)
l Chow, Andy H.F., Li Guang-Yu, and Ying Cheng-Shuo. Adaptive scheduling of mixed bus services with flexible fleet size assignment under demand uncertainty[J]. Transportation Research Part C: Emerging Technologies 2024, 158: 104452. (中科院1区)
l Yan Yi-Mo, Deng Yang, Cui Song-Yi, Kuo Yong-Hong*, Chow Andy H.F., Ying Cheng-Shuo. A policy gradient approach to solving dynamic assignment problem for on-site service delivery[J]. Transportation Research Part E: Logistics and Transportation Review 2023, 178: 103260. (中科院1区, AJG 3星)
l Lin Mei-Yan, Ma Li-Jun*, Ying Cheng-Shuo. Matching daily home health-care demands with supply in service-sharing platforms[J]. Transportation Research Part E: Logistics and Transportation Review 2021, 145: 102177. (中科院1区, AJG 3星)
l Nguyen Hoa T.M., Chow Andy H.F.*, Ying Cheng-Shuo. Pareto routing and scheduling of dynamic urban rail transit services with multi-objective cross entropy method[J]. Transportation Research Part E: Logistics and Transportation Review 2021, 156: 102544. (中科院1区, AJG 3星)
l Yan Yi-Mo, Chow Andy H.F., Ho Chin Pang, Kuo Yong-Hong*, Wu Qi-Hao, Ying Cheng-Shuo. Reinforcement learning for logistics and supply chain management: methodologies, state of the art, and future opportunities[J]. Transportation Research Part E: Logistics and Transportation Review 2022, 162: 102712. (中科院1区, AJG 3星)
