Andrea Cynthia Santos | Operational research | Research Excellence Distinction Award

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Prof. Dr. Andrea Cynthia Santos | Operational research | Research Excellence Distinction Award

Le Havre Normandie University, Engineering Institute of Logistics (ISEL) | France

Prof. Dr. Andréa Cynthia Santos is a leading scholar in Computer Science whose research lies at the intersection of Operations Research, urban systems, and large-scale disaster management. Her work focuses on developing advanced optimization models, algorithms, and decision-support approaches that address complex sociotechnical challenges in industrial, natural, and health-related crisis scenarios. She is widely recognized for contributions to integrated routing, scheduling, robust optimization, network design, drone-based search strategies, and humanitarian logistics in post-disaster environments. Her research is characterized by strong interdisciplinary engagement, combining computational optimization, artificial intelligence, and systems engineering to improve resilience and sustainability in modern cities. She has produced a substantial body of scientific work, including numerous international journal articles, book chapters, and conference contributions, and has played a key role in organizing international scientific events. Beyond her research activity, she has demonstrated significant leadership in academic administration and scientific strategy, including directing major institutional programs, steering research initiatives, and contributing to national and international committees. She has led multiple research projects across national, regional, industrial, and international collaborations, supported by multidisciplinary teams. Her supervision of PhD candidates, postdoctoral researchers, and master’s students reflects her strong commitment to academic mentorship and capacity building. She has also served as an evaluator for global research organizations and participated in expert panels spanning science, technology, and innovation. Her professional experience includes roles in academic governance, digital transformation, international relations, curriculum development, and research program management, positioning her as an influential figure in the fields of operations research, logistics innovation, and sustainable urban systems.

Profiles: Scopus | Orcid

Featured Publications

Barbalho, T. J., Jiménez Laredo, J. L., & Santos, A. C. (2025). The resource-constrained project scheduling problem for risk reduction after industrial disasters involving dangerous substances. OR Spectrum. Advance online publication.

Coco, A. A., Duhamel, C., Santos, A. C., & Haddad, M. N. (2024). Solving the probabilistic drone routing problem: Searching for victims in the aftermath of disasters. Networks, (July 2024).

Duhamel, C., & Santos, A. C. (2024). The strong network orientation problem. International Transactions in Operational Research.

Haddad, M. N., Santos, A. C., Duhamel, C., & Coco, A. A. (2023). Intelligent drone swarms to search for victims in post-disaster areas. Sensors, 23(23), 9540.

De Freitas, C. C., Aloise, D. J., Fontes, F. F. C., Santos, A. C., & Menezes, M. S. (2023). A biased random-key genetic algorithm for the two-level hub location routing problem with directed tours. OR Spectrum, 45, 1–26.

Jun Tang | Computer Science | Best Researcher Award

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Mr. Jun Tang | Computer Science | Best Researcher Award

AI Algorithm Researcher | Chengdu Zhihui Heneng City Technology | China

Mr. Jun Tang is a researcher specializing in intelligent transportation and autonomous driving, with a strong focus on the integration of computer vision and artificial intelligence to enhance vehicular perception and decision making systems. His research primarily explores large vision foundation models and their applications in object detection, scene understanding, and adaptive driving environments. He has contributed to developing advanced detection frameworks that leverage reinforcement learning to improve recognition accuracy, robustness, and real time responsiveness in dynamic traffic conditions. Mr. Tang’s recent interests include prompt-guided object detection methods that utilize natural language and contextual cues to refine visual understanding within autonomous systems. Through his work at Chengdu Zhihui Heneng City Technology, he plays a key role in bridging the gap between theoretical AI models and practical intelligent mobility applications, fostering innovations that advance the safety, efficiency, and scalability of next generation transportation systems. His interdisciplinary approach combines deep learning, machine perception, and cognitive automation, contributing to the development of more adaptive and human like decision making in autonomous vehicles.

Profile: Orcid

Featured Publications

Tang, J., Li, D., Yang, J., Chen, J., & Yuan, R. (2025). Leveraging large visual models for enhanced object detection: An improved SAM-YOLOv5 model. Knowledge-Based Systems, 114757.

Tang, J. (2025, August 29). RT-DETR-based intelligent transportation object detection optimization method and system with prompt mechanism fusion.

Tang, J. (2025, May 27). Object detection method and system based on prompt engineering and regional text description.

Tang, J. (2025, April 11). Quantitative evaluation method and system for multimodal large models.

Tang, J. (2025, January 17). Evaluation method and system for urban governance multimodal large models based on text labeling.