Search Results for author:
Found 6 papers, 5 papers with code
Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking
We develop a discrete-event simulation model, which we use to train and evaluate the proposed DRL approach.
Parcel loss prediction in last-mile delivery: deep and non-deep approaches with insights from Explainable AI
Within the domain of e-commerce retail, an important objective is the reduction of parcel loss during the last-mile delivery phase.
Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
We introduce an open-source GitHub repository containing comprehensive benchmarks for a wide range of machine scheduling problems, including Job Shop Scheduling (JSP), Flow Shop Scheduling (FSP), Flexible Job Shop Scheduling (FJSP), FJSP with Assembly constraints (FAJSP), FJSP with Sequence-Dependent Setup Times (FJSP-SDST), and the online FJSP (with online job arrivals).
Unsupervised anomaly detection algorithms on real-world data: how many do we need?
In this study we evaluate 32 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular datasets, performing the largest comparison of unsupervised anomaly detection algorithms to date.
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning
To address this, we introduce a Deep Reinforcement Learning (DRL) based approach called DR-ALNS that selects operators, adjusts parameters, and controls the acceptance criterion throughout the search.
On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors
Out-of-distribution (OOD) detection is concerned with identifying data points that do not belong to the same distribution as the model's training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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