Titre :

On balancing fairness and efficiency in routing of cooperative vehicle fleets

Auteur(s) :

López Sánchez, Aitor [Auteur]
Center for Intelligent Information Technologies and their Applications [CETINIA]
Lujak, Marin [Auteur]
Center for Intelligent Information Technologies and their Applications [CETINIA]
Semet, Frédéric [Auteur]
Integrated Optimization with Complex Structure [INOCS]
Billhardt, Holger [Auteur]
Center for Intelligent Information Technologies and their Applications [CETINIA]

Titre de la manifestation scientifique :

ATT 2022 - 12th International Workshop on Agents in Traffic and Transportation co-located with IJCAI-ECAI 2022 - 31st International Joint Conference on Artificial Intelligence and 25th European Conference on Artificial Intelligence

Ville :

Vienne

Pays :

Autriche

Date de début de la manifestation scientifique :

2022-07-23

Titre de la revue :

ATT 2022 Agents in Traffic and Transportation

Mot(s)-clé(s) en anglais :

Vehicle Routing Problem
multiple traveling salesman problem
intelligent vehicles
collaborative routing
fair and efficient routing

Discipline(s) HAL :

Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Système multi-agents [cs.MA]

Résumé en anglais : [en]

Shared economy takes an ever increasing part of our everyday activities. Generally, resource sharing is a key to more efficient and effective smart cities and transportation, with the most known applications in car sharing ...
Lire la suite >Shared economy takes an ever increasing part of our everyday activities. Generally, resource sharing is a key to more efficient and effective smart cities and transportation, with the most known applications in car sharing and cooperative hot meal delivery (Uber, Deliveroo, Uber Eats, Glovo, etc.). These fleets are generally composed of self-concerned individually rational agents (drivers) whose interest, in general, is their own efficiency and effectiveness, but also the fairness of the system as a whole; in other words, how their individual gain relates to the gain of the others. Most of the AI state-of-the-art fleet coordination approaches focus only on the efficiency of the fleet as a whole and result in generally unfair solutions without guarantees of the distribution of the workload, cost, or profit or without guarantees on the difference in performance between the worst-off and the best-off vehicle in the fleet. In this light, in this paper, we study the multiple Traveling Salesman problem (mTSP) and propose its two new variations that maximise utilitarian, egalitarian, and elitist social welfare and balance workload and efficiency of the fleet. Moreover, we give examples of how the proposed models influence routes of a fleet's vehicles in small but sufficiently representative problem instances. The computational results show a great diversity of routes depending on the social welfare approach considered. Thanks to the latter, we can balance solutions based on the efficiency and fairness requirements of a fleet at hand.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL