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Found 9 papers, 4 papers with code
PARCO: Learning Parallel Autoregressive Policies for Efficient Multi-Agent Combinatorial Optimization
Multi-agent combinatorial optimization problems such as routing and scheduling have great practical relevance but present challenges due to their NP-hard combinatorial nature, hard constraints on the number of possible agents, and hard-to-optimize objective functions.
Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
We consider the challenging problem of estimating causal effects from purely observational data in the bi-directional Mendelian randomization (MR), where some invalid instruments, as well as unmeasured confounding, usually exist.
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
To fill this gap, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 23 state-of-the-art methods and more than 20 CO problems.
Extend the FFmpeg Framework to Analyze Media Content
This paper introduces a new set of video analytics plugins developed for the FFmpeg framework.
Formulating and solving integrated order batching and routing in multi-depot AGV-assisted mixed-shelves warehouses
Due to the complexity of the integrated problem, we develop a novel variable neighborhood search algorithm to solve the integrated problem more efficiently.
An Intelligent CNN-VAE Text Representation Technology Based on Text Semantics for Comprehensive Big Data
CNN is used to extract the features of text vector to get the semantics among words and VAE is introduced to make the text feature space more consistent with Gaussian distribution.
Efficient order picking methods in robotic mobile fulfillment systems
In this paper, we concentrate on decisions about the assignment of pods to stations and orders to stations to fulfill picking for each incoming customer's order.
Deterministic Pod Repositioning Problem in Robotic Mobile Fulfillment Systems
Usually there are multiple shelves at the pick station.
Path planning for Robotic Mobile Fulfillment Systems
This paper presents a collection of path planning algorithms for real-time movement of multiple robots across a Robotic Mobile Fulfillment System (RMFS).
