Crises such as pandemics, natural disasters, and mass casualty incidents place immense pressure on emergency medical services to ensure rapid and effective patient care. Optimized ambulance routing is essential for these operations, as it directly influences response times, patient survival rates, and the overall resilience of healthcare systems. This study proposes a hybrid approach combining the Non-Dominated Sorting Genetic Algorithm III and automated machine learning, specifically the Tree-Based Pipeline Optimization Tool, to solve the priority-oriented ambulance routing problem. The Non-Dominated Sorting Genetic Algorithm III is used to minimize treatment completion times and prioritize critically ill patients, while the Tree-Based Pipeline Optimization Tool automates the generation of predictive models for probabilistic routing factors such as patient priority and traffic conditions. By integrating the optimization capabilities of evolutionary algorithms with machine learning, the proposed method improves both operational efficiency and equitable healthcare delivery. Experimental results show that the hybrid approach outperforms traditional methods in optimizing ambulance routes and prioritizing patients effectively.

In this article, we present a comprehensive study regarding the problem of allocating a fleet of electric vehicles to charging stations according to charging time and battery constraints. Each charging station’s capacity as well as the necessary charging time is known in advance while each vehicle’s arrival time is provided by a GPS device. We provide an integer programming model solved with an exact method to effectively handle this combinatorial problem along with a set of metaheuristic algorithms. To evaluate the performance of this solution framework, computational experiments are conducted on large-scale randomly generated instances simulating a real-world scenario.

The Ambulance Routing Problem (ARP) is a critical challenge in emergency medical services, aiming to efficiently allocate ambulances to patients. This research addresses the problem as a multi-objective optimization problem with real-world significance. The research methodology involves formulating ARP as a mathematical model, considering constraints such as patient-to-ambulance assignment, ambulance routing, and the requirement for ambulances to depart from point-of-care (PoC) locations. The NP-hard nature of ARP necessitates innovative solution approaches to handle its combinatorial complexity. This study proposes the application of the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) as a metaheuristic to tackle ARP by simultaneously minimizing total travel cost and completion time. NSGA-III is applied to optimize the formulated model, seeking a set of Pareto-optimal solutions that trade-off between total travel cost and completion time, and it is then evaluated using benchmark instances.

We provide a patient-centered hospital emergency allocation model in this work. Assigning each patient to the nearest hospital in accordance with resource availability restrictions is the aim of this study. We assume that necessary resource information to manage the emergency (beds and medical staff) are known in real time, and that every patient’s arrival time is supplied by a GPS device. Our contribution is to assess multiple meta-heuristic methods in order to effectively address this combinatorial problem. The goal of the Hospital Patient Allocation Problem (HPA) is to reduce the overall amount of time needed for patient emergency care. Computational experiments are carried out on massively simulated cases to evaluate the effectiveness of the investigated methodologies.

Today, providing patients in need of emergency care with high-quality pre-hospital care is a crucial part of society. Pre-hospital emergency care refers to all Emergency Medical Services (EMSs) that patients get outside of a hospital before being transported to the closest hospital. EMS refers to a variety of related tasks, such as dispatch, ambulance response to the scene, triage, and ambulance transfer to a care facility. EMS is essential in saving lives and lowering mortality and morbidity rates. EMS is a crucial political component of healthcare since it frequently serves patients in need of urgent care's first point of contact with the healthcare system. any lag time in access Any delays in receiving treatment can mean the difference between life and death. The increasing demands placed on emergency services have prompted them to implement a number of initiatives, including tighter integration of urgent care in other healthcare settings. This study seeks to offer a bibliography and a thorough analysis of the most well-known EMS optimization models put out for effective global healthcare. We emphasize the methodologies used to solve the cutting-edge models.

This paper presents a novel Best Fit Decreasing (BFD) algorithm for virtual machine (VM) placement, formulated as a bin packing problem in cloud data centers. The algorithm focuses on minimizing resource wastage by strategically allocating VMs to physical servers. It operates in two stages: first, sorting the VMs in descending order of resource requirements, and second, searching for the best-fit server based on available resources. Extensive simulations using real-world workload traces demonstrate that the BFD algorithm outperforms popular placement strategies, reducing the number of servers required and achieving higher packing efficiency. Comparative analysis against First Fit Decreasing (FFD) and Best Fit (BF) algorithms reveals consistent superiority in terms of resource utilization and packing efficiency. The proposed BFD algorithm offers an effective solution for VM placement, improving resource utilization and system efficiency in cloud data centers. Its findings can guide the development of practical VM placement algorithms, leading to enhanced resource management and cost savings in cloud computing environments.

In this paper, we present a charging station allocation model for electric vehicles. The goal is to assign each electric vehicle to the closest charging station with respect to capacity and charging time constraints. We assume that each vehicle's arrival time is provided by a GPS device and each charging station capacity as well as the required charging time are known in advance. We propose an integer programming model solved with CPLEX to efficiently deal with this combinatorial problem. The objective of Electric Vehicles Charging Stations Allocation (EVCSA) is to minimize the total required time from a start point to a destination going through a Charging Station (CS). To evaluate the performance of the proposed approach, computational experiments are conducted on large scale randomly generated instances simulating a real world scenario.

The efficient management of ambulance routing for emergency requests is vital to save patients when a disaster response scenario occurs. In today’s road traffic, the transportation of patients from emergency points becomes more difficult in such tragic situation. We consider an urgent situation where a lot of hurts/patients require simultaneously an urgent medical care. Patients are either (1) slightly injured which can be assisted on the spot or (2) seriously injured which should be transferred to hospitals. In this paper, we aim to enhance the response-time performance of emergency medical service providers by handling the ambulance routing problem (ARP). The problem can be modeled as either the Open Vehicle Routing Problem (OVRP) or a Vehicle Routing Problem with Pickup and Delivery (VRPPD). We propose a cluster-first route-second algorithm based on the Petal algorithm and the particle swarm optimization (PSO) approach in order to handle efficiently the ARP.

The spatial character intrinsic to the routing field requires the integration of geographic information systems (GIS) and optimization approaches to handle spatial and non-spatial data in transportation applications. Motivated by the need to better support decision making in logistic area, we develop an interactive spatial decision support system (SDSS) for solving the vehicle routing problems by coupling the simulated annealing method with Quantum GIS (QGIS). In this paper, the evoked variants of VRP are detailed and formulated mathematically. The SDSS architecture is designed for the VRPs showing the interaction of GIS and SA approach according to the tight coupling strategy. A VRP variant termed the Open VRP (OVRP) is selected to show the system effectiveness. The computational performance of the SDSS for the OVRP, based on a set of benchmark instances, turned out to be effective on both computation time and solution quality.