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.

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.

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.