U-M IOE associate professor, Siqian Shen, has received research funding from Ford to explore how mathematical optimization and operations research approaches can improve services that integrate ridesharing with goods delivery.
Siqian Shen, associate professor, U-M Industrial and Operations Engineering (IOE), has received research funding from Ford Motor Company to explore how mathematical optimization and operations research approaches can be used to design and improve services that integrate ridesharing with goods delivery.
Shared mobility systems like ridesharing have become widely used to deliver goods and services to customers whose orders usually arrive dynamically in real time — meaning that a business receives orders from customers once operations are already underway rather than ahead of time. As a result, the design and operations of such systems need to be highly flexible, while guaranteeing cost-effective solutions (including resource allocation, consolidation, and vehicle dispatch) and a high quality-of-service.
“The main challenge is how to combine our prior work on static system layout and operations design, with stochastic routing and scheduling decisions, to allow real-time vehicle dispatch and demand response,” said Shen.
“The main challenge is how to combine our prior work on static system layout and operations design, with stochastic routing and scheduling decisions, to allow real-time vehicle dispatch and demand response.”
Siqian Shen
Associate Professor, U-M Industrial & Operations Engineering
The results of the study will be applied to Ford Non-Emergency Medical Transportation (NEMT) to utilize the idle time of vehicles and enable the delivery of medicine or standard medical care such as flu shots to individual’s homes.
“We will push the modeling and computational frontiers of stochastic dynamic programming techniques to seek easy-to-implement policies and quick-but-good-quality solutions,” said Shen. “These solutions cannot be obtained by the traditional dynamic programming approach.”
Shen joined the U-M IOE in 2011. Her research area is integer programming, stochastic programming, and network optimization. The models she considers usually feature stochastic parameters and discrete decision variables. Applications of her research include risk analysis and optimization of energy, healthcare, cloud-computing, and transportation systems.
