Time: 14:30 on November 2, 2020
Venue: Room 0411, Teaching Building 0#, Jiuli Campus

Event Details:

Lecturer: Assistant Professor Zhang, Yu from Southwestern University of Finance and Economics

About the Lecturer:

Zhang, Yu is currently an associate professor and doctoral supervisor of Southwestern University of Finance and Economics. He is also the doctor of Northeastern University, the Ph.D. jointly cultivated by National University of Singapore. He was invited to visit the National University of Singapore as a researcher many times. He mainly engages in Robust Optimization and its application research in Logistics, Supply Chain, Transportation, and Medical Operation Management. He Presides over one project of the National Natural Science Foundation of China and participates in many other projects. As the first author, he has published many academic papers in Operations Research, Mathematical Programming, European Journal of Operational Research, Omega and other journals. He won the "Excellent Doctoral Dissertation" Award from the Society of Management Science and Engineering (10 nationally) in 2019.

About the Lecture:

For the vehicle routing problem with time windows, the lecturer considers the uncertain travel time, generates its empirical distribution through historical data, assumes that the real but unobservable distribution which is in a Wasserstein sphere with the empirical distribution as the center of the sphere, and plans the worst distribution of the robust vehicle routing scheme, in order to avoid the risk of late arrival as much as possible under the premise of a given cost budget. In this regard, the lecturer proposes a decision criterion called the Service Satisfaction Risk Index, which can simultaneously consider the probability of being late and the duration, characterize the risk and ambiguity of travel time, and can be evaluated analytically. In order to solve this problem, the lecturer proposes an accurate Branch & Cut Method, and a Variable Neighborhood Search Meta-heuristic Algorithm, and explores its acceleration calculation strategy. A large number of calculation experiments show that this method greatly improves the on-time arrival rate under the premise of slightly higher cost. In terms of solving efficiency, this method is superior to the traditional methods of minimizing the probability of late arrival and minimizing the length of late arrival.