A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
JAMPR learns to solve complex VRP with time windows.
New machine learning pipeline solves dynamic vehicle routing problems efficiently.
DPDP combines neural heuristics with DP for vehicle routing problems.
A framework for reinforcement learning tackles CVRP with competitive results.
New system assigns vehicles to routes for cost and energy efficiency.
A new dynamic attention model improves vehicle routing problem solutions.
Learning how to automatically solve optimization problems has the potential to provide the next big leap in optimization technology. The performance of automatically learned heuristics on routing problems has been steadily improving in recent years, but approaches based purely on machine learning are still outperformed…
Deep learning solves EV routing with time windows for EV fleets.
Two heuristics solve dynamic multiple travelling salesmen problems.
Do you remember your first video game console? We remember ours. Decades ago, they provided hours of entertainment. Now, we have repurposed them to solve dynamic and stochastic optimization problems. With deep reinforcement learning methods posting superhuman performance on a wide range of Atari games, we consider the …
Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majo…
Search-based methods for hard combinatorial optimization are often guided by heuristics. Tuning heuristics in various conditions and situations is often time-consuming. In this paper, we propose NeuRewriter that learns a policy to pick heuristics and rewrite the local components of the current solution to iteratively i…
World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation routing i.e., finding safest routes is largely an overlooked paradigm. In recent years, large amount of …
We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using reinforcement learning. In this approach, we train a single model that finds near-optimal solutions for problem instances sampled from a given distribution, only by observing the reward signals and following feasibility rules. Our mo…
Equity-Transformer solves NP-hard min-max routing problems efficiently.
SECRM-2D improves RL-based autonomous driving with safety guarantees.
Predicts destinations and routes from partial trajectory data.
Two DRL policies collaborate to solve NP-hard routing problems.
Sym-NCO leverages symmetricities to improve DRL-NCO performance.
Proposes a graph neural network for efficient multi-agent routing.
A novel controller for wheeled robots handles joystick inputs for smooth steering.
The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development. However, to push this idea towards practical implementation, we need better models and better ways of training. We contribute in both directions: we propose a model based on attention …
In this paper, we consider same-day delivery with vehicles and drones. Customers make delivery requests over the course of the day, and the dispatcher dynamically dispatches vehicles and drones to deliver the goods to customers before their delivery deadline. Vehicles can deliver multiple packages in one route but trav…
Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make…
Autonomous terrestrial vehicles must be capable of perceiving traffic lights and recognizing their current states to share the streets with human drivers. Most of the time, human drivers can easily identify the relevant traffic lights. To deal with this issue, a common solution for autonomous cars is to integrate recog…
Increasing availability of vehicle GPS data has created potentially transformative opportunities for traffic management, route planning and other location-based services. Critical to the utility of the data is their accuracy. Map-matching is the process of improving the accuracy by aligning GPS data with the road netwo…
Global routing has been a historically challenging problem in electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with wires without violating the design rules for the printed circuit boards or integrated circuits. Similar routing problems also exist in the de…
Deep learning has revolutionized the ability to learn "end-to-end" autonomous vehicle control directly from raw sensory data. While there have been recent extensions to handle forms of navigation instruction, these works are unable to capture the full distribution of possible actions that could be taken and to reason a…
Convolutional neural networks are the most widely used deep learning algorithms for traffic signal classification till date but they fail to capture pose, view, orientation of the images because of the intrinsic inability of max pooling layer.This paper proposes a novel method for Traffic sign detection using deep lear…
Proposes a new model for predicting future motion of road actors in autonomous vehicles.
A new router uses attention-based reinforcement learning to solve detailed routing problems efficiently.
Optimal crypto asset routing with CFMMs, including fixed costs.
An active area of research is to increase the safety of self-driving vehicles. Although safety cannot be guarenteed completely, the capability of a vehicle to predict the future trajectories of its surrounding vehicles could help ensure this notion of safety to a greater deal. We cast the trajectory forecast problem in…
A new approach integrates inventory prediction and routing optimization for better supply chain management.
Unified routing and arbitrage with concave continuation.
Proposes CPO framework for robust decision-making with explainable uncertainty regions.
Linear-time graph optimization using reinforcement learning.
Proposes CTSDG model for better vehicle intention prediction across domains.
Generative model learns vehicle trajectory distributions for better data generalization.
Deep learning improves vehicle control performance and generalizes well.
This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the probl…
New approach uses dynamic programming to efficiently discover failures in autonomous vehicle simulations.
This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is prop…
New method tackles parcel routing with AI.
Simultaneously estimates travel times and route choice model parameters.
Safe RL for autonomous vehicles using PCPO with trust regions and parallel learners.
Optimizes query routing to LLMs under cost and resource constraints.
Deep learning predicts vehicle behavior for safer autonomous driving.