Driver Equitability and Customer Optimality in Intelligent Vehicles Applications
Dr. Martin Aleksandrov – 2022
We consider classical vehicle routing problems with customer costs, vehicle feasibilities, driver profits, and driver responsiveness. We motivate a new template for these new problems, which first returns some feasible matching between drivers and customers and then some feasible plan for routing the vehicles through their matched locations. Thus, by using this template, we show that bounded equitability for drivers and Pareto optimality for customers can always be achieved in isolation but not always in combination. Finally, we give fixed-parameter tractable routing algorithms for fleet equitability and fleet efficiency.
Titel
Driver Equitability and Customer Optimality in Intelligent Vehicles Applications
Verfasser
Verlag
Springer, Cham
Schlagwörter
Multi-agent systems; Social choice; Vehicle routing
Datum
2022-09-13
Kennung
DOI: 10.1007/978-3-031-16474-3_26; Print ISBN: 978-3-031-16473-6
Quelle/n
Erschienen in
Proceedings of the 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Portugal, August 31-September 2, 2022, Proceedings. Eds. by Goreti Marreiros, Bruno Martins, Ana Paiva, Bernardete Ribeiro, and Alberto Sardinha. LNCS 13566, pages 309-321
Sprache
eng
Größe oder Länge
12 pages
Rechte
Martin Aleksandrov was supported by the DFG Individual Research Grant on “Fairness and Efficiency in Emerging Vehicle Routing Problems” (497791398).
BibTeX Code
@InProceedings{10.1007/978-3-031-16474-3_26,
author="Aleksandrov, Martin",
editor="Marreiros, Goreti
and Martins, Bruno
and Paiva, Ana
and Ribeiro, Bernardete
and Sardinha, Alberto",
title="Driver Equitability and Customer Optimality in Intelligent Vehicle Applications",
booktitle="Progress in Artificial Intelligence",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="309--321",
abstract="We consider classical vehicle routing problems with customer costs, vehicle feasibilities, driver profits, and driver responsiveness. We motivate a new template for these new problems, which first returns some feasible matching between drivers and customers and then some feasible plan for routing the vehicles through their matched locations. Thus, by using this template, we show that bounded equitability for drivers and Pareto optimality for customers can always be achieved in isolation but not always in combination. Finally, we give fixed-parameter tractable routing algorithms for fleet equitability and fleet efficiency.",
isbn="978-3-031-16474-3"
}
author="Aleksandrov, Martin",
editor="Marreiros, Goreti
and Martins, Bruno
and Paiva, Ana
and Ribeiro, Bernardete
and Sardinha, Alberto",
title="Driver Equitability and Customer Optimality in Intelligent Vehicle Applications",
booktitle="Progress in Artificial Intelligence",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="309--321",
abstract="We consider classical vehicle routing problems with customer costs, vehicle feasibilities, driver profits, and driver responsiveness. We motivate a new template for these new problems, which first returns some feasible matching between drivers and customers and then some feasible plan for routing the vehicles through their matched locations. Thus, by using this template, we show that bounded equitability for drivers and Pareto optimality for customers can always be achieved in isolation but not always in combination. Finally, we give fixed-parameter tractable routing algorithms for fleet equitability and fleet efficiency.",
isbn="978-3-031-16474-3"
}