The objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) award is to improve medical preparedness, public safety and security at mass gathering events through the use of optimization methodologies. The nation engages in over 500 mass gathering events, such as marathons, each year. Such events are subject to medical emergencies for the participants, and other security related events. This GOALI project brings together engineering and medical faculty at Northwestern University and the organizers of the Bank of America Chicago Marathon to study approaches for the mitigation of hazards and risks through course design. In the case of a marathon, course design decisions are related to the route to be followed and the locations of aid stations, medical tents and volunteers on the course. Multi-objective models and solution approaches will be developed for course design, coupled with data analytics and field observations to identify a safe and medically accessible course. The research plan is based on active integration across the areas of modeling, algorithms, data analysis and decision maker engagement.
If successful, the results of this research will lead to advances in medical preparedness and response for a variety of mass gathering events. This project provides a test bed to advance the science and practice of mass event planning and preparedness, through repeated field observations at the Chicago Marathon, conducted by faculty, students, and practitioners. Given connections across city of Chicago and within the marathon community, the research team will host seminars to disseminate best practices from this research to other mass events in the Chicago region and worldwide. This GOALI project represents a unique application of operations research that will expose students to a new type of planning problems. The operations research modeling will lead to new developments in multi-objective arc routing models, by introducing new classes of arc routing problems and creating solution methods for these problems. Further, this work will lead to new approaches to multi-objective optimization based on the iterative generation of promising solutions with input from decision makers.
