We have developed a methodology for allocating operating room capacity to specialties. Our methodology consists of a finite-horizon mixed integer programming (MIP) model which determines a weekly operating room allocation template that minimizes inpatients' cost measured as their length of stay. A number of patient type priority (e.g., emergency over non-emergency patient) and clinical constraints (e.g., maximum number of hours allocated to each specialty, surgeon and staff availability) are included in the formulation. The optimal solution from the analytical model is inputted into a simulation model that captures some of the randomness of the processes (e.g., surgery time, demand, arrival time, and no-show rate of the outpatients) and non-linearities (e.g., the MIP assumes proportional allocation of demand satisfaction (output) with room allocation (input)). The simulation model outputs the average length of stay for each specialty and the room utilization. On a case example of a Los Angeles County Hospital, we show how the hospital length of stay pertaining to surgery can be reduced.
Managing the dynamic behavioral changes of a production plant process to keep to a production plan is a challenge and requires the ability to predict the dynamic behavior of processes and alter any controls, as needed, to adhere as closely as possible to the plan. This paper presents a novel solution (called Cognitive Plant Advisor) based on the use of advanced machine learning to learn complex dynamics from sensor data coupled with mathematical programming to optimize the operations of a production plant. The Cognitive Plant Advisor provides set point recommedations for a 12-72 hour horizon to (i) improve throughput, or (ii) provide optimal recovery plan for a disruption. This advisory system has the potential to improve throughput by upto 1% of total production.
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