Theme: Ensuring the robustness of link flow observation systems in sensor failure events

Lecturer: Professor Zhu Ning (College of Management and Economics, Tianjin University)

Host: Professor Niu Ben (College of Management, Shenzhen University)

Time: 10:30 am -11:30 pm, January 8, 2024 (Monday)

Location: A203, Mingli Building, Lihu Campus, Shenzhen University

Lecturer profile: Zhu Ning, Professor of College of Management and Economics at Tianjin University. His main research direction are the operation management and optimization of transportation and logistics systems. He has published over 40 papers, some of which have been published in top journals in the field of management science and transportation management, such as M&SOM, IJOC, TS, TR Part B/C/E, EJOR, Journal of Scheduling, Systems Engineering Theory and Practice, and Journal of Systems Engineering. He hosted 4 National Natural Science Foundation projects (including Excellent Youth Program, General Program, etc.) and 2 Ministry of Education projects. He serves as the Deputy Editor in Chief of the Journal of Transportation Engineering and Information, and being a member of the 2nd Transportation Management Sub Committee of the Society of Management Science and Engineering.

Lecture Summary: Transportation network link flow data are an intuitive information for monitoring the traffic condition of the entire network, and can be used to enhance traffic management and control. Link flow observation systems are typically designed using flow conservation equations to obtain the information of flow on unobserved links by inference. The occurrence of sensor failures in such systems may lead to flow information loss on both observed and inferred links. Most studies on this issue have considered sensor deployment and failure evaluation as separate processes. In contrast, in this study, both processes are integrated to establish a link flow observation system that withstands sensor failures. First, we propose a novel model to solve the sensor location problem for full link flow observability. The proposed model is then modified to evaluate the link flow information loss in sensor failure event, and incorporated into adistributional robust optimization (DRO) model for the sensor location problem concerned. The DRO model minimizes the worst-case expected information loss of the system during the planning horizon with different types of sensors. Moreover, we extend the DRO model to a target-based version, into which a convex risk measure named Observation fulfillment risk index is introduced to evaluate the risk of failing to meet the predetermined observation target for any sensor installation schemes. The devised models can be directly solved by commercial solvers for networks like Nguyen-Dupuis, and ametaheuristic genetic algorithm is designed for large-scale example networks. Numerical experiments are performed for networks with different sizes. The DRO model generates robust sensor location schemes with worst-case performances that are superior to those achieved using benchmark methods, such as stochastic programming. The use of the Observation fulfillment risk index enhances the system stability and target fulfillment level and decreases the standard deviation of the link flow information loss. We also make use of numerical experiments to derive some insightful conclusions on installation budget, coverage ratio, failure risks, etc.