Abstract
Emergency departments (ED) are recognized as the most complex system, as they provide different activities of care to many patients, highly correlated and interacting resources, and finally the associated uncertainty resulting from those activities at the ED at a different time. Applying decision support methodologies helps to improve the decision makers’ (DM) decisions. We aim to introduce an advanced multi-objective optimization (MOO) model that achieves ED’s objective by modifying the mathematical model covered in [1] by adding additional objectives and constraints to take into consideration the historical data of the patient’s length of stay (LOS) and time to meet a doctor (TMD). The proposed framework combines simulation with optimization to give better results. Simulation is used to simulate the system behavior by using discrete event simulation (DES) gives a real picture of the current situation and problem, changing DES parameters generates many what-if scenarios that will be used as an input for the optimization model. NSGA-II algorithm is used as a Multi-Objective Optimization (MOO) technique to achieve DE’s objectives by generating a set of Pareto optimal solutions. Based on our experimental results, EDs’ overcrowding issue can be solved for high population countries and non urgent cases by decreasing the patient’s LOS which is calculated from the patient’s arrival till he leaves the ED to be a maximum of 4 h meeting a doctor within 1 h from arrival and be triaged (TTT) within 10 mins from arrival.
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El-Sawy, N.M., Ali, D.S., Saleh, M., Mohamed, O.M.S. (2023). Improving the Emergency Department by Using Simulation and Optimization. In: Hassanien, A.E., Snášel, V., Tang, M., Sung, TW., Chang, KC. (eds) Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022. AISI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-031-20601-6_35
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