Recently, Professor Fu Chenyi from the School of Management, as the first author, has published online the research paper entitled "Robust Road-Side Unit Location Problem" in Production and Operations Management. His co-authors include Professor Zhu Ning from the University of Science and Technology of China and Associate Professor Zhou Minglong from Fudan University. Production and Operations Management is a top journal in the field of operations management and one of the 24 leading business school academic journals selected by the University of Texas at Dallas (known as UTD24).

This study focuses on deploying road-side units (RSUs) in road networks to improve the efficiency of traffic information collection and processing for autonomous driving. Existing research on RSU location problems under uncertainty remains limited, and most existing RSU location models in dynamic edge computing scheduling ignore uncertainties in task transmission and model parameters. To address this gap, this study proposes a two-stage robust satisficing RSU location model considering uncertain demand for computing tasks and stochastic delays in task processing and transmission. The optimization model aims to minimize the violation risk of capacity constraints and task latency thresholds. To solve the model, the robust RSU location model is reformulated into an equivalent mixed-integer convex optimization model solvable via the cutting-plane algorithm, and two acceleration algorithms are designed to improve computational efficiency. Numerical experiments demonstrate that the proposed robust model has good computational feasibility and outperforms deterministic models and queuing theory models across multiple evaluation metrics. Experimental results also reveal significant differences in RSU deployment plans generated by different models. Specifically, the robust model can effectively reduce computing task transmission volume, average completion time, as well as the magnitude and probability of capacity constraint violations in both grid networks and randomly generated networks.

About the speaker:Fu Chenyi is Professor and Doctoral Supervisor in the Department of Management Science and Engineering, School of Management. His research interests include robust optimization and urban traffic operation optimization. He has 12 published or accepted journal papers as first or corresponding author, including one paper in Operations Research (UTD24) and one in Production and Operations Management (UTD24), both top journals in management science, six papers in Transportation Research Part B, a top journal in transportation operations research, and three papers in European Journal of Operational Research, a high-level journal in operations optimization. He has won the Best Paper Award and Outstanding Doctoral Dissertation Award from the International Symposium on Computational Transportation Science. He currently presides over one NSFC Young Scientists Fund Project and one Fundamental Research Funds for the Central Universities (Social Sciences) Project.

(Written by:Fu Chenyi Reviewed by:Jia Ming)