Multi-criteria capacity optimization in emergency departments
The proposed project focuses specifically on capacity optimization in emergency departments (EDs). It is novel in the sense that we (1) explicitly account for bed capacity as well as personnel capacity, (2) take into account many realistic aspects that impact patient flow through the ED (such as patients leaving the ED without treatment when the waiting times are too long, or patients remaining blocked when no beds are available in the inpatient unit), and (3) consider the problem as a constrained stochastic multi-objective optimization problem, where the traditional objective of minimizing the capacity cost is complemented with objectives and constraints that reflect the quality of care provided. Despite its relevance in many real-life settings, the literature on constrained stochastic multi-objective optimization is rather scarce. We intend to develop and test two novel simulation-optimization approaches (a metamodel-based approach and a model enhancement approach), that aim to outperform existing heuristics in terms of effectiveness (i.e., ability to provide a high-quality approximation of the Pareto frontier), and efficiency (computational effort). The performance will be evaluated using real-life data from the ED department of a large regional hospital in Belgium.
Period of project
01 September 2018 - 30 June 2019