Caiyue ZHAO

Caiyue ZHAO

Ph.D. student at The Chinese University of Hong Kong

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Hi, I am a Ph.D. student at HKU, hope that I can enjoy this journey.

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A data-driven system for cooperative-bus route planning based on generative adversarial network and metric learning

Published in Annals of Operations Research, 2022

Abstract

Faced with dynamic and increasingly diversified public transport requirements, bus operators are urged to propose operational innovations to sustain their competitiveness. In particular, ordinary bus operations are heavily constrained by well-established route options, and it is challenging to accommodate dynamic passenger flows effectively and with a good level of resource utilization performance. Inspired by the philosophy of sharing economy, many of the available transport resources on the road, such as minibuses and private vehicles, can offer opportunities for improvement if they can be effectively incorporated and exploited. In this regard, this paper proposes a metric learning-based prediction algorithm which can effectively capture the demand pattern and designs a route planning optimizer to help bus operators effectively deploy fixed routing and cooperative buses with traffic dynamics. Through extensive numerical studies, the performance of our proposed metric learning-based Generative Adversarial Network (GAN) prediction model outperforms existing ways. The effectiveness and robustness of the prediction-supported routing planner are well demonstrated for a real-time case. Further, managerial insights with regard to travel time, bus fleet size, and customer service levels are revealed by various sensitivity analysis.

Recommended citation: Wang, J., Zhang, Y., Xing, X., Zhan, Y., Chan, W. K. V., & Tiwari, S. (2022). A data-driven system for cooperative-bus route planning based on generative adversarial network and metric learning. Annals of Operations Research, 1-27.

A Two-stage Learning-based method for Large-scale On-demand pickup and delivery services with soft time windows

Published in Transportation Research Part C: Emerging Technologies, 2023

Abstract

With the rapid growth of the on-demand logistics industry, large-scale pickup and delivery with soft time windows has become widespread in various time-critical scenarios. This problem has proven to be an NP-hard problem. Hence, the computation time and resources required to solve it increase exponentially with the growth of size. As a result, it is burdensome for the exact algorithm and heuristic method to generate a high-quality solution instantly. Machine learning seems to be a possible option due to the advantage of offline training. However, it is difficult to solve large-scale problems due to the lengthy training time, heavy computational cost, and training instability. Thus, this paper proposes the two-stage learning-based method composed of the clustering stage and the routing stage to tackle this problem. The clustering stage builds upon the attention mechanism by introducing graph convolutional network to the input, which can keep the match of pickup and paired delivery customers and classify them into different vehicles, while the routing stage adopts a well-trained model to generate a route for each capacitated vehicle. Furthermore, the well-trained model is utilized to train another problem inspired by transfer learning. Experiments show that the model, trained on small-scale problems, generalizes well to larger-scale problems, and achieves superior performance compared with the heuristic method and Google OR-Tools, with an extremely short computing time. In addition, the favorable transferability of this model is verified through contrast experiment, which can save a significant amount of training time.

Recommended citation: Zhang, K., Li, M., Wang, J., Li, Y., & Lin, X. (2023). A Two-stage Learning-based method for Large-scale On-demand pickup and delivery services with soft time windows. Transportation Research Part C: Emerging Technologies, 151, 104122.

End-to-end Optimization for a Compact Optical Neural Network Based on Nanostructured 2× 2 Optical Processors

Published in IEEE Photonics Journal, 2023

Abstract

Recent research in silicon photonic chips has made huge progress in optical computing owing to their high speed, small footprint, and low energy consumption. Here, we employ nanostructured 2 × 2 optical processors in an optical neural network for implementing a binary classification task efficiently. The proposed optical neural network is composed of five linear layers including ten optical processors in each layer, and nonlinear activation functions. 2 × 2 optical processors are designed based on digitized meta-structures which have an extremely compact footprint of 1.6 × 4 μm 2 . A brand-new end-to-end design strategy based on Deep Q-Network is proposed to optimize the optical neural network for classifying a generated ring data set with better generalization, robustness, and operability. A high-efficient transfer matrix multiplication method is applied to simplify the calculation process in traditional optical software. Our numerical results illustrate that the maximum and mean accuracy on the testing data set can reach 90.5% and 87.8%, respectively. The demonstrated optical processors with a significantly compact area, and the efficient optimization method exhibit high potential for large-scale integration of whole-passive optical neural network on a photonic chip.

Recommended citation: Zhao, C., Wang, J., Mao, S., Liu, X., Kin, W., Chan, V., & Fu, H. Y. (2023). End-to-end Optimization for a Compact Optical Neural Network Based on Nanostructured 2× 2 Optical Processors. IEEE Photonics Journal.

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