Optimization of simulation models for resource planning in hospitals accepted as GECCO Challenge
Our proposal to organise the competition "Optimization of a simulation model for a capacity and resource planning task for hospitals under special consideration of the COVID-19 pandemic" is accepted for the Competitions of GECCO 2021, see https://gecco-2021.sigevo.org
Similar to the many previous competitions, the team of the Institute of Data Science, Engineering, and Analytics at the TH Cologne (IDE+A), hosts the 'Industrial Challenge' at the GECCO 2021.
This year’s industrial challenge is posed in cooperation with an IDE+A partner from health industry and with Bartz & Bartz GmbH.
Simulation models are valuable tools for resource usage estimation and capacity planning. Your goal is to determine improved simulation model parameters for a capacity and resource planning task for hospitals. The simulator, babsim.hospital, explicitly covers difficulties for hospitals caused by the COVID-19 pandemic. The simulator can handle many aspects of resource planning in hospitals:
- various resources such as ICU beds, ventilators, personal protection equipment, staff, pharmaceuticals
- several cohorts (based on age, health status, etc.).
The task represents an instance of an expensive, high-dimensional computer simulation-based optimization problem and provides an easy evaluation interface that will be used for the setup of our challenge. The simulation will be executed through an interface and hosted on one of our servers (similar to our last year's challenge).
The task is to find an optimal parameter configuration for the babsim.hospital simulator with a very limited budget of objective function evaluations. The best-found objective function value counts. There will be multiple versions of the babsim.hospital simulations, with slightly differing optimization goals, so that algorithms can be developed and tested before they are submitted for the final evaluation in the challenge.
The participants will be free to apply one or multiple optimization algorithms of their choice.
Thus, we enable each participant to apply his/her algorithms to a real problem from health industry, without software setup or licensing that would usually be required when working on such problems.