Publications

Refereed Journal Articles

• Azucena, J.C.H., Wang, H., Jin, Y. and Liao, H., Modeling and analysis of two Normal populations based on an unlabeled paired sample. Communications in Statistics - Simulation and Computation. 2022. DOI: https://doi.org/10.1080/03610918.2022.2134895
• Azucena, J.C.H., Alkhaleel, B., Liao, H. and Nachtmann, H., Hybrid simulation to support interdependence modeling of a multimodal transportation network, Simulation Modelling Practice and Theory, Vol. 107, pp. 102237, 2021. DOI: https://doi.org/10.1016/j.simpat.2020.102237

Articles in Refereed Conference Proceedings

Published

• Aghamohammadghasem, M., Azucena, J.C.H., Hashemian, F., Liao, H., Zhang, S., and Nachtmann, H.L., System Simulation and Machine Learning-Based Maintenance Optimization for an Inland Waterway Transportation System, Proceedings of the 2023 Winter Simulation Conference. San Antonio, TX. December 10-13, 2023
• Aghamohammadghasem, M., Azucena, J.C.H., Hashemian, F., Liao, H., Zhang, S., and Nachtmann, H.L., Preventive Maintenance Planning for an Inland Waterway Transportation System Using Deep Reinforcement Learning, Proceedings of the IISE Annual Conference and Expo 2023. New Orleans, LA. May 20-23, 2023
• Azucena, J.C.H., Hashemian, F., Liao, H. and Pohl, E.A., Applying Machine Learning to Improve All-Terminal Network Reliability, Proceedings of the 69^th Annual Reliability and Maintainability Symposium. Orlando, FL. January 23-26, 2023
• Azucena, J.C.H., Wells, H., Liao, H., Sullivan, K. and Pohl, E.A., Applying Deep Reinforcement Learning to Improve the Reliability of an Infrastructure Network, Proceedings of the 60^th ESReDA Seminar: Advances in Modelling to Improve Network Resilience. France. May 4-5, 2022
• Bipasha, T., Azucena, J.C.H., Alkhaleel, B., Liao, H. and Nachtmann, H., Hybrid Simulation to Support Interdependence Modeling of a Multimodal Transportation Network, Proceedings of the 2019 Winter Simulation Conference. National Harbor, MD. December 8-11, 2019
• Azucena, J.C.H. and Liao, H., Prognostic Using Dual-Stage Attention-Based Recurrent Neural Networks, Proceedings of the 11^th International Conference on Mathematical Methods in Reliability. Hong Kong. June 3-7, 2019

Working Articles

• Hashemian, F., Azucena, J.C.H., Liao, H. and Pohl, E.A., Convolutional Graph Neural Networks for Reliability Improvement of All-terminal Networks. Reliability Engineering and Systems Safety. Forthcoming.