Hybrid Simulation Modelling of Emergency Departments for Resource Scheduling

ABSTRACT

This paper addresses the problem of resource scheduling of emergency departments in an uncertain environment. An innovative approach based on Agent-Based Simulation (ABS) and Discrete Event Simulation (DES) is proposed. To do so, this paper first proposes a framework integrating ABS and DES. Subsequently, we provide a detailed description of the DES and ABS parts of the model of the ED. Five combinations of strategies – namely, “FIFO+Random” ($i.e.$ FIFO: the queue of patients operates in FIFO; Random: the patients are randomly assigned to doctors), “FIFO+Centralised”, “Random+Centralised” (“Centralised” implies the assignment of the most appropriate doctor to a patient is only determined by the Platform agent via optimising all the resources), “Random+Random” and “Autonomous” – are implemented to schedule the resources of the ED. Finally, experiments analyse the efficiency of the five combinations of strategies on the duration of patients’ stay. The results show that “FIFO+Centralised” outperforms others regarding the average for the duration of patients’ stay without hospitalisation: 3.75% of the time has been saved; “Random+Centralised” outperforms others regarding the average for the duration of patients’ stay with hospitalisation: 0.57% of the time has been saved.

KEYWORDS:

• Emergency Department (ED)
• resource scheduling
• hybrid simulation modelling
• Agent-Based Simulation (ABS)
• Discrete-Event Simulation (DES)

Acknowledgments

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. https://www.gouvernement.fr/info-coronavirus/carte-et-donnees accessed on 22 September 2020.

2. https://www.acep.org/practre.aspx?Id=29156 accessed on 22 September 2020.

3. https://www.anylogic.com accessed on 1 November 2022.

4. https://www.viapatient.fr/ght-loire-avec-viapatient/accessed on 11 January 2023.