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Found 22 papers, 10 papers with code
Multi-Agent Soft Actor-Critic with Global Loss for Autonomous Mobility-on-Demand Fleet Control
We study a sequential decision-making problem for a profit-maximizing operator of an Autonomous Mobility-on-Demand system.
Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shifts
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.
Contextual Stochastic Vehicle Routing with Time Windows
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.
Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
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.
Strategic Workforce Planning in Crowdsourced Delivery with Hybrid Driver Fleets
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.
Ambulance Demand Prediction via Convolutional Neural Networks
Many models exist to optimize operational tasks such as ambulance allocation and dispatching.
Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces
Large discrete action spaces (LDAS) remain a central challenge in reinforcement learning.
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time Windows
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.
Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
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.
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
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.
Bilevel Optimization for Feature Selection in the Data-Driven Newsvendor Problem
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.
Coordinating charging request allocation between self-interested navigation service platforms
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.
Support Vector Machines with the Hard-Margin Loss: Optimal Training via Combinatorial Benders' Cuts
The classical hinge-loss support vector machines (SVMs) model is sensitive to outlier observations due to the unboundedness of its loss function.
Optimal Decision Diagrams for Classification
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.
Coordinated Charging Station Search in Stochastic Environments: A Multi-Agent Approach
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.
Electric vehicle charge scheduling with flexible service operations
Specifically, time-ineffective recharging operations limit the profitability of charging during service operations such that operators recharge vehicles off-duty at a central depot.
On the Interplay between Self-Driving Cars and Public Transportation
Cities worldwide struggle with overloaded transportation systems and their externalities.
The Customer is Always Right: Customer-Centered Pooling for Ride-Hailing Systems
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.
A Concise Guide on the Integration of Battery Electric Buses into Urban Bus Networks
With the increasing market penetration of battery-electric buses into urban bus networks, practitioners face many novel planning problems.
On the Integration of Battery Electric Buses into Urban Bus Networks
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.
Born-Again Tree Ensembles
The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives.
On the Interaction between Autonomous Mobility on Demand Systems and Power Distribution Networks -- An Optimal Power Flow Approach
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.
