Decision support system with simulation-based optimization for healthcare capacity planning

Abstract

Capacity management of hospital staff and other resources is an important challenge faced by a healthcare administrator. Because of the variation in service times and the inability to inventory services, capacity buffers are required to ensure reasonable waiting times for patients. The nonlinear relationship between resource utilization and patient wait times makes it difficult to determine the optimal capacity buffer, called the knee. This work concerns the development of a decision support system using Python to determine optimal capacity buffers using a Monte Carlo simulation and knee optimization model that allows for flexibility in specifying uncertain arrival patterns and service times. Key factors relating to the system's size, amount of service time variation, and arrival patterns are shown to affect optimal buffer sizes. The system shows users their current status and where changes need to be made to the service times or the number of servers to achieve optimal results.