2 code implementations • 10 Apr 2024 • Zeno Woywood, Jasper I. Wiltfang, Julius Luy, Tobias Enders, Maximilian Schiffer
We study a sequential decision-making problem for a profit-maximizing operator of an Autonomous Mobility-on-Demand system.
1 code implementation • 15 Feb 2024 • Tobias Enders, James Harrison, Maximilian Schiffer
We study the robustness of deep reinforcement learning algorithms against distribution shifts within contextual multi-stage stochastic combinatorial optimization problems from the operations research domain.
no code implementations • 10 Feb 2024 • Breno Serrano, Alexandre M. Florio, Stefan Minner, Maximilian Schiffer, Thibaut Vidal
We study the vehicle routing problem with time windows (VRPTW) and stochastic travel times, in which the decision-maker observes related contextual information, represented as feature variables, before making routing decisions.
1 code implementation • 14 Dec 2023 • Heiko Hoppe, Tobias Enders, Quentin Cappart, Maximilian Schiffer
We study vehicle dispatching in autonomous mobility on demand (AMoD) systems, where a central operator assigns vehicles to customer requests or rejects these with the aim of maximizing its total profit.
no code implementations • 28 Nov 2023 • Julius Luy, Gerhard Hiermann, Maximilian Schiffer
Against this background, we jointly study a workforce planning problem that considers fixed drivers (FDs) and the temporal development of the crowdsourced driver (CD) fleet over a long-term time horizon.
no code implementations • 8 Jun 2023 • Maximiliane Rautenstrauß, Maximilian Schiffer
Many models exist to optimize operational tasks such as ambulance allocation and dispatching.
1 code implementation • 31 May 2023 • Fabian Akkerman, Julius Luy, Wouter van Heeswijk, Maximilian Schiffer
Large discrete action spaces (LDAS) remain a central challenge in reinforcement learning.
1 code implementation • 3 Apr 2023 • Léo Baty, Kai Jungel, Patrick S. Klein, Axel Parmentier, Maximilian Schiffer
With the rise of e-commerce and increasing customer requirements, logistics service providers face a new complexity in their daily planning, mainly due to efficiently handling same day deliveries.
1 code implementation • 8 Feb 2023 • Kai Jungel, Axel Parmentier, Maximilian Schiffer, Thibaut Vidal
Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and transportation-related pollution.
1 code implementation • 14 Dec 2022 • Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer
We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system.
no code implementations • 12 Sep 2022 • Breno Serrano, Stefan Minner, Maximilian Schiffer, Thibaut Vidal
The lower-level problem learns the optimal coefficients of the decision function on a training set, using only the features selected by the upper-level.
no code implementations • 19 Aug 2022 • Marianne Guillet, Maximilian Schiffer
While such fleet-optimized charging station visit recommendations may alleviate local bottlenecks, they can also harm the system if self-interested navigation service platforms seek to maximize their own customers' satisfaction.
1 code implementation • 15 Jul 2022 • Ítalo Santana, Breno Serrano, Maximilian Schiffer, Thibaut Vidal
The classical hinge-loss support vector machines (SVMs) model is sensitive to outlier observations due to the unboundedness of its loss function.
no code implementations • 28 May 2022 • Alexandre M. Florio, Pedro Martins, Maximilian Schiffer, Thiago Serra, Thibaut Vidal
Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound to grow exponentially with their depth.
no code implementations • 26 Apr 2022 • Marianne Guillet, Maximilian Schiffer
We model a multi-agent stochastic charging station search problem as a finite-horizon Markov decision process and introduce an online solution framework applicable to static and dynamic policies.
no code implementations • 11 Jan 2022 • Patrick Sean Klein, Maximilian Schiffer
Specifically, time-ineffective recharging operations limit the profitability of charging during service operations such that operators recharge vehicles off-duty at a central depot.
no code implementations • 3 Sep 2021 • Nicolas Lanzetti, Maximilian Schiffer, Michael Ostrovsky, Marco Pavone
Cities worldwide struggle with overloaded transportation systems and their externalities.
no code implementations • 2 Jul 2021 • Paul Karaenke, Maximilian Schiffer, Stefan Waldherr
We study the benefit of this mechanism from a customer, fleet operator, and system perspective, and compare it to existing provider-centered pooling (PCP) mechanisms.
no code implementations • 21 Apr 2021 • Nicolas Dirks, Dennis Wagner, Maximilian Schiffer, Grit Walther
With the increasing market penetration of battery-electric buses into urban bus networks, practitioners face many novel planning problems.
no code implementations • 22 Mar 2021 • Nicolas Dirks, Maximilian Schiffer, Grit Walther
By analyzing the impact of battery capacities and charging power on the optimal fleet transformation, we show that medium-power charging facilities combined with medium-capacity batteries are superior to networks with low-power or high-power charging facilities.
1 code implementation • ICML 2020 • Thibaut Vidal, Toni Pacheco, Maximilian Schiffer
The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives.
1 code implementation • 1 May 2019 • Alvaro Estandia, Maximilian Schiffer, Federico Rossi, Justin Luke, Emre Can Kara, Ram Rajagopal, Marco Pavone
Specifically, we extend previous results on an optimization-based modeling approach for electric AMoD systems to jointly control an electric AMoD fleet and a series of PDNs, and analyze the benefit of coordination under load balancing constraints.