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Redeployment in Convoys of Fleets of Shared Vehicles Jan-Thierry Wegener

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Redeployment in Convoys of Fleets of Shared Vehicles Jan-Thierry Wegener Redeployment in Convoys of Fleets of Shared Vehicles Jan-Thierry Wegener To cite this version: Jan-Thierry Wegener. Redeployment in Convoys of Fleets of Shared Vehicles. Other [cs.OH]. Univer- sité Blaise Pascal - Clermont-Ferrand II, 2016. English. NNT : 2016CLF22722. tel-01584052 HAL Id: tel-01584052 https://tel.archives-ouvertes.fr/tel-01584052 Submitted on 8 Sep 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. No d’ordre : D.U 2722 EDSPIC : 767 UNIVERSITÉ BLAISE PASCAL – Clermont-Ferrand II ECOLE DOCTORALE SCIENCES POUR L’INGÉNIEUR DE CLERMONT-FERRAND Thèse présentée par Jan-Thierry WEGENER pour obtenir le grade de DOCTEUR D’UNIVERSITÉ Specialité : Informatique Redeployment in Convoys of Fleets of Shared Vehicles Redéploiement en convois de flottes de véhicules partagés Soutenue publiquement le 26 juillet 2016 devant le jury : Présidente : Prof. Fatiha Bendali - Université Blaise Pascal, France Rapporteurs : Prof. Nabil ABSI - Ecole des Mines de Saint-Etienne, France Prof. Aziz Moukrim - Université de Technologie de Compiègne, France Directeurs de thèse : Prof. Alain Quillot - Université Blaise Pascal, France Prof. Annegret K. Wagler - Université Blaise Pascal, France Invité : Prof. Sven O. Krumke - TU Kaiserslautern, Germany Declaration This dissertation has been completed by Jan-Thierry Wegener under the supervision of Profes- sor Alain QUILLIOT and Professor Annegret K. WAGLER and has not been submitted for any other degree or professional qualification. I declare that the work presented in this dissertation is entirely my own except where indicated by full references. Jan-Thierry Wegener i Acknowledgment This work was founded by the French National Research Agency, the European Commission (Feder funds) and the Région Auvergne within the LabEx IMobS3. Without their generous financial support this thesis would never have been possible. I wish to express my sincere thanks to my supervisor Prof. Annegret Wagler for the continuous support of my PhD study and related research. She always finds a way to make a complex thought understandable for everyone and I hope to have gained a bit of this ability as well. I could not image to have finished this thesis without her immense patience, motivation and guidance throughout the years of research and while writing this thesis. My sincere thanks also goes to Prof. Alain Quilliot for his great advises during my research and for sharing his expertise. I also deeply thank Prof. Sven Krumke for giving several thoughtful insights into the topic of Online Optimization and for the many fruitful discussion. I would like to thank Prof. Louchka Popova who strongly encouraged me to write the thesis. Furthermore, I would like to thank all of my colleges at the university for their discussions and the good time I spend at the laboratory. Hereby I want to give my special thanks to Sahar Bsaybes, Maxime Bombrun, Abdeslem Belghoul, Kaoutar Ghazi, Lakhdar Akroun, Benoit Bernay, and Romain Pogorelcnik. I also like to thank my friends, especially, Markus Langer for the support and the great time we spend together. I would also like to take this opportunity to express my gratitude to my family for their support during the years of study. Especially, I would like to give my wholehearted thanks my mother for her unlimited support and encouragement. My thanks also goes to my half-brother Manuel Möbius who always supported me whenever possible. My deepest gratitude goes to my beloved partner Celine Patin for her loving and support through this venture. Her encouragement when the times got rough are much appreciated. iii Abstract Carsharing is a modern way of car rental, attractive to customers who make only occasional use of a car on demand. In a carsharing system, a fleet of cars is distributed at specified stations in an urban area, customers can take a car at any time and station and return it at any time and station, provided that there is a car available at the start station and a free place at the destination station. To ensure the latter, customers have to book their demands in advance. For operating such a system in a satisfactory way, the stations have to keep a good ratio between the total number of places and the number of cars in each station, in order to serve as many requests as possible. This leads to the problem of balancing the load of the stations, called Relocation Problem: an operator has to monitor the load and to decide when and how to move cars from “overfull” stations to “underfull” ones. We consider an innovative carsharing system, where the cars are partly autonomous, which allows to build wireless convoys of cars leaded by a special vehicle, such that the whole convoy is moved by only one driver. This setting is similar to bikesharing, where trucks can simultaneously move several bikes during the relocation process. In this thesis, we address the dynamic and static aspects of the Relocation Problem. The “Dynamic Relocation Problem” describes the situation when cars can be moved between stations during the working hours in order to satisfy the needs of the customers. Hereby, the operator has to make decisions dynamically according to the current situation. In the “Static Relocation Problem” we assume that there is no (or only little) interaction by customers with the system. This situation occurs when the carsharing system is prepared for the next day, i.e., the relocation process is performed during the night. We model the Relocation Problem in the framework of a metric task system. Afterwards, we theoretically analyze both problems and give strategies to solve them. Finally, we perform some computational experiments to examine the applicability of the presented algorithms in practice. Keywords: carsharing; partly autonomous cars; Relocation Problem; online optimization; heuristic; network flows v Résumé L’autopartage est une fa¸onmoderne pour louer une voiture ; l’autopartage est attractive pour des clients qui utilisent une voiture seulement occasionnellement. Dans un système d’autopartage, une flotte de vehicules est distributée dans une area urbaine. Les client peuvent prendre une voiture á tout moment et tout stations et ils peuvent la retourner á tout moment et tout stations, á condition qu’il y a une voiture librée á la station de départ et aussi bien qu’il y a une place de parking librée á la station de destination. Pour assurer le dernier, les clients peuvent rèserver une voiture en avance. Pour opérer tel qu’un système de manière satisfaisante, il faut que le ratio de numéro de vehicules et de numéro de place librée dans les stations est equilibré. Cela conduit au problème de balancer de charge des stations, appelé problème de relocalisation : un opérateur doit surveiller le charge et decider quand et dans quelle manière les voiture doivent etre deplacer d’une station « trop plein » á une station « insuffisante plein ». Nous considérons un système d’autopartage innovative, où les voitures sont partiellement autonomes. Ceci permit des construire des convois de vehicules, dirigé par une vehicule spécial tel que un convoi est déplacé par seulement un conducteur. Cette configuration est similaire á un système de vélos en libre-service, oú un camion peut déplacer plusieurs vélos simultanément pendant le processus de la relocalisation. Dans le cadre de cette these, nous étendons les aspects dynamique et statique du problème de relocalisation. Le « problème de relocalisation dynamique » décrit la situation lorsque les voiture sont déplacé pendant les heures de travail afin de satisfaire les besoins des clients. L’operateur doit prendre des décisions dynaimques en fonction de la situation. Dans le cadre du « problème de relocalisation statique », nous supposons qu’il y a aucune (ou seulement un peu) d’interaction par des clients avec le system. Cette situation se produit lorsque le système est préparé pour le lendemain, par example lorsque le processus de la relocalisation est effectuée pendant la nuit. Nous modélisons le problème de relocalisation dans le framework d’un système de tâches métriques. Ensuite, nous analysons les deux problèmes et nous donnons des stratégies pour les résoudre. Enfin, nous effectuons quelques expériences de calcul pour examiner l’applicabilité des algorithmes présentés en pratique. Mots clés : autopartage ; voitures partiellement autonomes ; problème de relocalisation ; optimisation en ligne ; rśeau de flot vii Contents I Introduction 1 1 State of the Art 7 1.1 Establishing New Systems . 7 1.2 Customer Behavior . 8 1.3 Routing and Balancing Problems . 9 1.4 Online Optimization . 12 1.4.1 Online Problems . 13 1.4.2 Online Strategies . 14 1.4.3 Online Analysis . 17 2 Metric Task System: Modeling Framework for the Relocation Problem 21 3 Outline 31 II Dynamic Relocation Problem 33 4 Decision Problems 35 4.1 Minimizing the Number of Rejected Customer Requests . 35 4.1.1 Competitive Analysis . 36 4.1.2 Max/Max Ratio . 41 4.2 Maximizing the Number of Accepted Customer Requests . 41 4.2.1 Competitive Analysis . 42 4.2.2 Max/Max Ratio . 52 5 Optimization Problems 55 5.1 Minimizing the Waiting Time . 55 5.1.1 Competitive Analysis . 56 5.1.2 Max/Max Ratio . 60 5.2 Minimizing the Total Tour Length . 60 5.2.1 Competitive Analysis . 61 5.2.2 Max/Max Ratio . 66 6 Computational Results 67 6.1 Computing an Optimal Offline Solution . 68 viii Contents 6.2 A Flow-Based Heuristic .
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