MILP solvers can be used in a variety of contexts, including ride-hailing services, electric grid operators, vaccine distributors, and any other organization confronted with a difficult resource allocation issue.
“In a field like optimization, it is common for people to envision solutions as purely machine learning or purely classical at times. Senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE) and a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS), states, “I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach.”
Together with co-lead authors Wenbin Ouyang, a CEE graduate student, and IDSS graduate student Sirui Li, Wu wrote the paper; as well as Max Paulus, an alumni understudy at ETH Zurich. The examination will be introduced at the Gathering on Brain Data Handling Frameworks.