The concept of Digital Twin (DT) seems promising to improve the management of patient pathways in Emergency Departments (EDs). This article proposes an agent-based architecture of a DT designed for that purpose. The core of this DT is its Information System (IS), which is regularly synchronised on the IS of the Physical Twin (PT). The agents model the ED’s resources (equipment and staff) and patients in the DT and update this information in the DT’s IS. This article shows how such a DT may operate in three modes: (0) “Digital Shadow” to monitor the ED’s current state in real time, (1) “Synchronised DT” to monitor the ED’s current and future states according to a predictive simulation, and (2) “Exploratory DT” in order to perform Monte Carlo simulations of various future states. Mode (1) is the main contribution. This proposition is illustrated in a simulation of the ED in order to demonstrate the capabilities.

This study addresses dynamic cell configuration and labor assignment by making a trade-off between economic, social and environmental criteria which is called as sustainability configuration. In order to improve the study about energy loss, a new bi-objective mathematical model is presented. The first objective of model minimizes cost criteria and the second one energy loss which is represented by the efficiency of each machine. We develop a Non-dominated Sorting Genetic Algorithm (NSGA-II) in order to solve our model. The applicability of our approach is illustrated by two test problems.

The Emergency Department (ED) is a principal sector in the hospital that has to make crucial decisions effectively under uncertainties to ensure that the patients receive good quality care. Improving decision-making in ED, especially the resource scheduling decision, becomes one research interest. A promising approach to improve decision-making is by utilising Digital Twin (DT). This context of decision-making in DT is both (i) an opportunity given by the DT which provides data close to real-time and (ii) constrained by the DT which forces to make decisions dynamically rather than statically. That is, our research contributes to the decision layer of DT. More precisely, we propose a decentralised organisation to enhance the resource scheduling decision-making process in ED. This is a multi-agent system with a local dynamic resource scheduling based on a Mixed Integer Linear Programming (MILP) model. This proposition is compared to two other organisations, which are a centralised organisation that optimises the dynamic scheduling MILP globally, and the FIFO sequencing currently performed in the paediatric ED of the University Hospital Centre in Saint-Étienne, France. The results show that the proposed decentralised organisation provides good results quickly and seems promising for improving resource scheduling decision-making in ED with DT support. Note to Practitioners —This research provides an initial study on the approach to improve the decision-making process in the Emergency Department (ED) of the hospital by using Digital Twin (DT). We focus more on improving resource scheduling decisions, which is a part of the decision layer of DT. We propose a decentralised organisation based on a multi-agent approach for solving the resource scheduling problem. Each resource agent, i.e. nurse, can run a local dynamic resource scheduling based on a Mixed Integer Linear Programming (MILP) model. Through our proposition with a centralised model and current practice in ED based on the triage-based First in First Out (FIFO) rule, we can see that our decentralised organisation is a favourable decision organisation in the context of decision improvement with DT, as it is capable of providing favourable results in a short time. This research will be further extended by including different resources and tasks in ED to make the model closer to reality in ED.