A review on simulation-based metamodeling in emergency healthcare: methodology, applications, and future challenges

Abstract

Simulation-based metamodels or surrogate models are simplified models that capture the relationship between inputs and outputs of the simulation model. The analytical expression of metamodels is defined using a sample of input/output points obtained from the simulation model. This analytical expression is then embedded in an optimization process that usually provides solutions much faster than other simulation-based optimization techniques such as metaheuristics or mathematical modeling. The goal of this paper is to describe the simulation-based metamodeling approach and to provide a thorough review of the literature on its applications to emergency healthcare systems. For this purpose, we examine the recent literature (journals and conference proceedings) published in the last 15 years (2008–2022). Finally, we identify findings and avenues of research in simulation-based metamodeling that deserve special attention from the scientific community and allow the potential of this approach to be used for better decision-making in emergency healthcare.

A data-driven approach of layout evaluation for electric vehicle charging infrastructure using agent-based simulation and GIS

“…Risco-Martin et al 52 introduced a discrete-event simulation for real-time monitoring and management of harmful algal and cyanobacterial blooms (HABs), addressing the need for efficient detection and response to HABs threatening water quality in dynamic environments. The application of simulation-based methodology in emergency healthcare can be seen in the review article by Sahlaoui et al 53 Similar to the EVCI location problem in this paper, these studies use simulation methods to explore real-world issues in urban environments from temporal and spatial perspectives. In these simulation models, some studies employ deterministic mathematical formulas to represent the relationships between key factors, while others utilize agent-based approaches to simulate complex systems through rules governing interactions between agents and the environment to make predictions.…”

Ontology-based crisis simulation system for population sheltering management

“…To address the challenges identified in the crisis management process and enhance our ability to collect data, develop simulation models, and contextualize situations, 8,9,24 this work adopts an ontology-driven approach. The use of an ontology serves the purpose of formalizing intricate knowledge from various stakeholders including municipal's decision-makers and citizens/volunteers, in order to establish a shared semantic framework within the domain of crisis management.…”

“…Routing optimizer: it finds the best route for vehicles and this is computed through OpenStreetMap. The results are used by the constraint-based recommender system in Equation (9), which is the optimization goal of the recommender system. 4.2.3.…”

“…More specifically, the interface represents the required information of two rescue points as shown in Figure 8. Then, the optimizer in Figure 7 receives the information about rescue points and the information about available citizen-volunteer driver/ vehicle resources in the knowledge base data model, as well as the information about estimated time and distance to move between vehicle and rescue point using OpenStreetMap data, which has to be minimized, see Equation (9). In sequence, the constraint-based recommender system calculates and recommends an optimal solution with the suggestion of a list of pairs of driver/ vehicle for each rescue point as in Figure 9, note that constraints defined in Equations ( 4)-( 6) and ( 8) have to be satisfied.…”

“…Similarly, a boat owner with a capacity of six passengers could help evacuate five people in the event of a flood. Unfortunately, existing research on crisis management (e.g., evacuation simulation) 1,[3][4][5][6][7][8] has primarily focused on the use of public resources, such as ambulances 8,9 and fire trucks. 5 However, in some cases, these resources may not be available due to high demand or the remote location of the affected area.…”