Issam Zidi

In this article, we propose a novel two-phase approach to solve the Dynamic Ambulance Routing Problem (DARP), where a significant number of injured persons from different regions require treatment and medical assistance. In such cases, many people call for ambulances, but the availability and number of ambulances are insufficient to reach all patients simultaneously. Consequently, managing the ambulance fleet to respond to all demands as quickly as possible is a crucial research area to explore. In this research, we consider two main types of patients (Hard Emergency Injury [HEI] and Soft Emergency Injury [SEI]) in a dynamic context, where new demands may arise after the ambulance service has started. Furthermore, a mathematical model is proposed to formulate the DARP as a multi-objective problem that minimizes both the travel distance and ride time. To solve this NP-hard problem, we propose a two-phase approach, called k -means-MOPSO, which combines k -means clustering to group the injuries into geographic classes with the Multi-Objective Particle Swarm Optimization (MOPSO) heuristic to route the ambulances. To demonstrate the effectiveness of the k -means-MOPSO approach, this approach is compared with well-known state-of-the-art methods (NSGA-III, NSGA-II, and SPEA2) using various Pareto front metrics, such as Hypervolume, Spacing, and R2 Indicator.

Le Problème de Transport à la Demande (PTD), consiste à prendre en charge le transport des personnes d'un lieu de départ vers un lieu d'arrivée. Il est caractérisé par un ensemble de demandes de transport et d'un nombre de véhicules disponible. L'ultime objectif dans ce travail de thèse est d'offrir une alternative optimisée au déplacement individuel et collectif. Le PTD est classé parmi les problèmes NP-difficile, la majorité des travaux de recherche ont été concentrés sur l'utilisation des méthodes approchées pour le résoudre.Ce problème est également multicritère, la solution proposée dans ce travail permet à la fois une réduction du temps de voyage et également de la distance parcourue. Dans cette thèse, nous proposons notre contribution à l'étude et à la résolution du problème de transport à la demande multicritère et dynamique en appliquant l'algorithme de recuit simulé multi-objectif. Une grande partie de notre travail concerne la conception, le développement et la validation des approches qui permettent de donner des solutions optimales ou quasi optimales, pour un PTD. Ces approches utilisent une méthode multicritère qui s’appuie sur l’algorithme de recuit simulé. La modélisation du PTD est représentée par une architecture multi-acteurs. Cette architecture met en évidence l’aspect distribué du système ainsi que les interactions et les relations qui peuvent avoir lieu entre les différents acteurs. Nous présentons dans ce travail un Système Multi-Agents pour la planification des itinéraires des véhicules affectés au transport des voyageurs. Les agents de ce système utilisent le module d’optimisation développé dans la première partie

The problem treated in this work is the management of the home health care service. Especially, we studied the caregivers’ tours problem and the management of conflict sanitary visits. Home health care service is known as a care mode allowing patients suffering from evolving and complex diseases to benefit at home from medical and paramedical coordinated care that can be only provided in care institution. To solve this problem we proposed a multi-agents approach using three heuristics which optimize both the daily caregivers’ tours to minimize the travel costs and maximize the planned services in order to address potential conflicts. This approach was tested on as set of benchmarks from the literature and shows the efficiency of the proposed algorithms. After studying the various numerical calculated results, we concluded that the policy based on «the number of remaining patient to visit» gives better results in terms of resolving detected conflicts.

In this paper, we try to improve health care services by treating the variation among several issues related to the health field, particularly the Home Health Care Problem (HHCP). In fact, Home Health Care Service (HHCS) is known as a care mode allowing patients who suffer from complex and evolving diseases to benefit at home from medical and paramedical coordinated care that can be only provided in hospitals. In this work, we treat the Caregivers' Tours Problem (CTP) and conflict management sanitary visits to patients' homes. We developed a new three-phase metaheuristic, which optimizes both the daily caregivers' tours to minimize the travel costs and maximizes the planned services in order to address potential conflicts. The obtained numerical results, compared with those provided by the other existing approaches, are motivating and encouraging.

This paper presents a mathematical model that aims at describing and to resolving the Stochastic DRP with an approach based on the NSGAII. DRP consists in taking the passenger from a place of departure to a place of arrival. The ultimate aim is to offer an alternative to individually and collectively optimized displacements. The DRP is classified as NP-hard problem. That's why, most researches have been concentrated on the use of approximate methods to solve it. Indeed, the DRP is a multi-criteria problem. The proposed solution aims to reduce both route and duration in response to a certain quality of service provided. Actually, during the system of transport on demand (TOD) there are many problems that can inhibit the proper functioning of the system, such as failure of the car and bottling. In this work, we have contributed to the resolution of stochastic DRP in solving possible problems when touring vehicles such as bottling, vehicle failures, accidents and vehicle occupancy service. We have developed an approach which is based on the genetic algorithm kind of the kind NSGAII to optimize travel time and elapsed travel time during the shot, taking into account the possible rate of risk in this selected itinerary.

The paper describes a multi-objective mathematical model for the Dial a Ride Problem (DRP) and an application of Multi-Objective Simulated Annealing (MOSA) to solve it. DRP is to take over the passenger from a place of departure to a place of arrival. In the DRP, passengers send transportation requests to an operator. The ultimate aim is to offer an alternative to displacement optimized individually and collectively. The DRP is classified as NP-hard problem that's why most research has been concentrated on the use of approximate methods to solve it. Indeed the DRP is a multi-criteria problem, the proposed solution which aims to reduce both route duration in response to a certain quality of service provided. Actually, during the system of transport on demand (TOD) there are many problems can inhibit the proper functioning of the system, such as failure of the car and bottling etc. We propose a resolution of the dynamic DRP using a service of TOD called TOD with Simulated Annealing (TOD-SA). The solution proposed in this paper enables both a reduction of travel time also the distance traveled.

This paper describes an application of a Hybrid Algorithm based on the Multi-Objective Simulated AnnealingTabu Search (MOSA-TS) to solve the dynamic Dial a Ride Problem (DRP). In fact, different versions of the dynamic Dial k Ride Problem are found in every day practice; transportation of people in low-density areas, transportation of the handicapped and elderly persons and parcel pick-up and delivery service in urban areas. The problem is to affect every new passenger request to one of the vehicles and to design a new route and schedule for this vehicle. This affectation must be done in real time. In this work, we offer our contribution to the study and to solve the dynamic DRP in the application using a multi agent system based on Hybrid Algorithm: Multi-Objective Simulated Annealing -Tabu Search (MOSA-TS).