A discrete simulation-based optimization approach for multi-period redeployment in emergency medical services

Self Cite

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.

Section: Sbm Applications In Healthcarementioning

confidence: 99%

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

Sahlaoui,

Aboueljinane,

Lebbar

2023

Self Cite

“…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.…”

Section: Abs Approachesmentioning

confidence: 99%

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

Zhang,

Tan

2023

The development and popularization of new energy vehicles have become a global consensus. The shortage and unreasonable layout of electric vehicle charging infrastructure (EVCI) have severely restricted the development of electric vehicles. In the literature, many methods can be used to optimize the layout of charging stations (CSs) for producing good layout designs. However, more realistic evaluation and validation should be used to assess and validate these layout options. This study suggested an agent-based simulation (ABS) model to evaluate the layout designs of EVCI and simulate the driving and charging behaviors of electric taxis (ETs). In the case study of Shenzhen, China, geographical positioning system (GPS) trajectory data were used to extract the temporal and spatial patterns of ETs, which were then used to calibrate and validate the actions of ETs in the simulation. The ABS model was developed in a geographic information system (GIS) context of an urban road network with traveling speeds of 24 h to account for the effects of traffic conditions. After the high-resolution simulation, evaluation results of the performance of EVCI and the behaviors of ETs can be provided in detail and in summary. Sensitivity analysis demonstrates the accuracy of simulation implementation and aids in understanding the effect of model parameters on system performance. Maximizing the time satisfaction of ET users and reducing the workload variance of EVCI were the two goals of a multiobjective layout optimization technique based on the Pareto frontier. The location plans for the new CS based on Pareto analysis can significantly enhance both metrics through simulation evaluation.

“…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.…”

Section: Ontology-based Knowledge Representation For Crisis Managementmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

Ontology-based crisis simulation system for population sheltering management

Zhong,

Ngoc,

Negre

et al. 2023

Climate change has led to an increase in the frequency and intensity of natural disasters, necessitating the development of efficient crisis management strategies for population sheltering. However, existing research on this topic primarily focuses on the use of public resources such as ambulances and fire trucks, which may sometimes be insufficient due to high demand and impacted locations, worsening the shortage of resources. This research introduces an ontology-based crisis simulation system for population sheltering management that focuses on the integration and distribution of citizen–volunteer drivers/vehicles into the evacuation process. Recognizing the limitations of public resources in current crisis management models, our approach incorporates citizen resources to enhance overall evacuation capacity. We develop an ontology to standardize crisis management knowledge, frame vehicle distribution as a recommendation problem, and design a simulation module incorporating a constraint-based recommender system. The proposed scenario illustrates how the simulation system can recommend citizen resources during crisis situations by considering the constraints to be satisfied. With our system, we aim at helping stakeholders to be prepared for various disaster scenarios: optimizing resource allocation and reducing time to make decisions by decision-makers.