Solving a Dial-a-Ride Problem with a Hybrid Multi-objective Evolutionary Approach: Application to Demand Responsive Transport

International audienceDemand responsive transport allows customers to be carried to their destination as with a taxi service, provided that the customers are grouped in the same vehicles in order to reduce operational costs. This kind of service is related to the dial-a-ride problem. However, in order to improve the quality of service, demand responsive transport needs more flexibility. This paper tries to address this issue by proposing an original evolutionary approach. In order to propose a set of compromise solutions to the decision-maker, this approach optimizes three objectives concurrently. Moreover, in order to intensify the search process, this multi-objective evolutionary approach is hybridized with a local search. Results obtained on random and realistic problems are detailed to compare three state-of-the-art algorithms and discussed from an operational point of view

An Oriented Convergent Mutation Operator for Solving a Scalable Convergent Demand Responsive Transport Problem

International audienceThis paper presents a method for solving the convergence demand responsive transport problem, by using a stochastic approach based on a steady state genetic algorithm for enumerating a set of optimizing sprawling spanning trees, which constitute the best solutions to this problem. Specifically designed to speed up the convergence to optimal solutions, we introduce an oriented convergent mutation operator, allowing multi-objective considerations. So this solution lays the first stakes for considering real-time solving of such a problem. Led by computer science and geography laboratories, this study is provided with a set of experimental results evaluating the approach

Comparison of three algorithms for solving the convergent demand responsive transportation problem

International audienceLed by computer science and geography laboratories, this paper presents three algorithms for solving the Convergent Demand Responsive Transport Problem (CDRTP). Two of them are exact: the first one is based on a dynamic programming algorithm to enumerate exhaustively the sprawling spanning trees and the second one is based on a depth first search algorithm. The third one is stochastic and uses a steady state genetic algorithm. These approaches address the problems of scalability and flexibility, are compared and discussed

On Optimizing a Demand Responsive Transport with an Evolutionary Multi-Objective Approach

6 pagesInternational audienceThis paper deals with a dial-a-ride problem with time windows applied to a demand responsive transport service. An evolutionary approach as well as new original representation and variation operators are proposed and detailed. Such mechanisms are used with three state-of-the-art multi-objective evolutionary algorithms: NSGA-II, IBEA and SPEA2. After introducing the general problem, the solution encoding and the algorithm mechanisms are depicted. The approach is assessed by applying the algorithms to both random and realistic dial-aride instances. Then a statistical comparison is provided in order to highlight the most suited evolutionary algorithms to optimize real-life transportation problems

Application of a Co-evolutionary Genetic Algorithm to solve the Periodic Railway Timetabling Problem

International audienceIn train operations, a timetable is used to establish the departure and arrival times for the trains at the stations or other relevant locations in the rail network or a subset of this network. The elaboration of a timetable responds to the commercial needs of the customers, for both passenger and freight traffic, but also, it must respect some security and capacity constraints. The combination of these requirements and constraints makes the preparation of a yearly timetable a complex process that usually takes months to be fully completed. This paper addresses the problem of generating periodic timetables, which means that the trains concerned are operated on a recurrent pattern, e.g., trains of the same line will run every 30 minutes, we present a suitable constraint-based model of the problem. Furthermore, we propose a dedicated genetic algorithm, based on a co-evolutionary scheme with two populations, to create feasible and quality periodic timetables in short periods of time. Finally, two case studies are discussed, both of them representing a subset of the Netherlands railway network

Exposure to Phthalates and Phenols during Pregnancy and Offspring Size at Birth

Background: Data concerning the effects of prenatal exposures to phthalates and phenols on fetal growth are limited in humans. Previous findings suggest possible effects of some phenols on male birth weight

Birth Weight and Prenatal Exposure to Polychlorinated Biphenyls (PCBs) and Dichlorodiphenyldichloroethylene (DDE): A Meta-analysis within 12 European Birth Cohorts

Objectives: Exposure to high concentrations of persistent organochlorines may cause fetal toxicity, but the evidence at low exposure levels is limited. Large studies with substantial exposure contrasts and appropriate exposure assessment are warranted. Within the framework of the EU (European Union) ENRIECO (ENvironmental Health RIsks in European Birth Cohorts) and EU OBELIX (OBesogenic Endocrine disrupting chemicals: LInking prenatal eXposure to the development of obesity later in life) projects, we examined the hypothesis that the combination of polychlorinated biphenyls (PCBs) and dichlorodiphenyldichloroethylene (DDE) adversely affects birth weight

Optimisation de transport à la demande dans des territoires polarisés

This pluridisciplinary thesis focuses on Demand Responsive Transport (DRT). DRT is a ground collective public transport activated on demand only. This is a service at the cross of taxi and bus. Our main idea consists in introducing geographical models in computer optimization. Indeed, the polarized structure of territories contributes to deploy DRT in (multi)convergence by using spanning trees and gravity model. This approach rises also in rationalizing economic costs of the service (travelling durations, number of vehicles...) Moreover, this thesis provides a set of methodological elements for deploying a DRT using metaheuristics on the one hand (genetic algorithms, i.e. NSGA-II) and geography models on the other hand (convergence based on polarized structure of the territory).By the mean of flows convergence, the method uses graph theory to define vehicles routes, optimized by a multicriteria genetic algorithm with Pareto approach.The last part of the thesis focuses on the impact of metrics on the solutions, given a territory and a spatial granularity. This topic opens the problem of the link between the configuration, the territory and the use.Cette thèse pluridisciplinaire, géographique et informatique (géomatique), s'intéresse à la problématique du transport à la demande (TAD). Le TAD est un transport de personnes collectif terrestre activé seulement à la demande se situant à mi-chemin entre le taxi et le bus. L'idée porteuse de cette recherche est d'utiliser la structure polarisée des territoires pour faciliter une optimisation informatique d'un TAD en (multi)convergence, recourant, par exemple, aux Arbres Couvrants et au modèle gravitaire . Cette approche se traduit notamment par une rationalisation des coûts économiques du service (regroupement des clients, nombre de véhicules nécessaires, temps de parcours...). Par ailleurs, cette thèse donne des éléments méthodologiques pour déployer un TAD usant d'une part d'algorithmes à métaheuristiques (les algorithmes génétiques, i.e. NSGA-II) et d'autre part de modèles géographiques (la forme dite en convergence se basant sur le caractère polarisé du territoire). Des simulations permettent d'évaluer la capacité des méthodes développées à fournir de bonnes solutions dans un contexte opérationnel de forte montée en charge potentielle.Reposant sur le principe de convergence des flux, la méthode exploite la théorie des graphes pour définir les tournées des véhicules, elles-mêmes optimisées selon un algorithme génétique dédié, reposant sur une approche multicritères avec front de Pareto.La dernière partie de la thèse s'intéresse à l'influence du choix des métriques d'optimisation sur les solutions obtenues, compte tenu d'un territoire et d'une granularité spatiale donnés. Elle ouvre sur le questionnement suivant : quelle configuration d'optimisation pour quel territoire et pour quel usage

A deep learning Attention model to solve the Vehicle Routing Problem and the Pick-up and Delivery Problem with Time Windows

SNCF, the French public train company, is experimenting to develop new types of transportation services by tackling vehicle routing problems. While many deep learning models have been used to tackle efficiently vehicle routing problems, it is difficult to take into account time related constraints. In this paper, we solve the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) and the Capacitated Pickup and Delivery Problem with Time Windows (CPDPTW) with a constructive iterative Deep Learning algorithm. We use an Attention Encoder-Decoder structure and design a novel insertion heuristic for the feasibility check of the CPDPTW. Our models yields results that are better than best known learning solutions on the CVRPTW. We show the feasibility of deep learning techniques for solving the CPDPTW but witness the limitations of our iterative approach in terms of computational complexity