Task Scheduling

Task scheduling aims to optimize the allocation of resources to jobs, minimizing completion times and maximizing resource utilization. Current research focuses on developing sophisticated algorithms, including reinforcement learning (both single-agent and multi-agent), evolutionary computing, and constraint programming, often integrated with predictive models (e.g., using Large Language Models) to improve scheduling decisions in diverse contexts like manufacturing, cloud computing, and even astronomical observations. These advancements are crucial for improving efficiency and resource management across various industries and scientific domains, impacting everything from manufacturing throughput to the speed of large-scale model training.

Papers

January 8, 2024

Boosting Column Generation with Graph Neural Networks for Joint Rider Trip Planning and Crew Shift Scheduling
Jiawei Lu, Tinghan Ye, Wenbo Chen, Pascal Van Hentenryck
Graph Neural Network Task Scheduling Scheduling Problem Column Generation Mobility on Demand

January 4, 2024

December 21, 2023

A Constraint Programming Model for Scheduling the Unloading of Trains in Ports: Extended
Guillaume Perez, Gael Glorian, Wijnand Suijlen, Arnaud Lallouet
Task Scheduling Problem Solving Constraint Programming High Speed Train System Port Operation

December 19, 2023

Reasoning with random sets: An agenda for the future
Fabio Cuzzolin
Future Reasoning Task Scheduling Statistical Learning Theory Statistical Analysis Belief Function Random Set

December 17, 2023

An appointment with Reproducing Kernel Hilbert Space generated by Generalized Gaussian RBF as $L^2-$measure
Himanshu Singh
Task Scheduling Reproducing Kernel Hilbert Space Radial Basis Function Vector Machine

December 10, 2023

Multi-Agent Reinforcement Learning for Multi-Cell Spectrum and Power Allocation
Yiming Zhang, Dongning Guo
Multi Agent Reinforcement Learning Task Scheduling Observable Markov Decision Process Interaction Graph Multi Agent Proximal Policy Optimization

December 5, 2023

Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service
Meiying Zhang, Huan Zhao, Sheldon Ebron, Ruitao Xie, Kan Yang
Federated Learning Task Scheduling Client Selection Client Heterogeneity

November 29, 2023

Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning
Franco Terranova, M. Voetberg, Brian Nord, Amanda Pagul
Reinforcement Learning Offline Reinforcement Learning Large Scale Task Scheduling Deep Q Network Based Telescope

November 28, 2023

Multi-defender Security Games with Schedules
Zimeng Song, Chun Kai Ling, Fei Fang
Task Scheduling Security Game Multiple Defender Heterogeneous Zero Day Attack Stackelberg Security Game

November 27, 2023

Scheduling and Communication Schemes for Decentralized Federated Learning
Bahaa-Eldin Ali Abdelghany, Ana Fernández-Vilas, Manuel Fernández-Veiga, Nashwa El-Bendary, Ammar M. Hassan, Walid M. Abdelmoez
Gradient Descent Critical Lesson Task Scheduling Decentralized Federated Learning Modulation Scheme Decentralized Framework

November 21, 2023

Scheduling Distributed Flexible Assembly Lines using Safe Reinforcement Learning with Soft Shielding
Lele Li, Liyong Lin
Reinforcement Learning Action Space Safe Reinforcement Learning Task Scheduling Scheduling Problem Assembly Line Novel Shielding Mechanism

November 20, 2023

Data-driven project planning: An integrated network learning and constraint relaxation approach in favor of scheduling
Izack Cohen
Task Scheduling Temporal Constraint Network Learning

October 23, 2023

Scrap Your Schedules with PopDescent
Abhinav Pomalapally, Bassel El Mabsout, Renato Mansuco
Task Scheduling Hyper Parameter Hyper Parameter Tuning Ferrous Scrap

October 11, 2023

Imitation Learning from Observation with Automatic Discount Scheduling
Yuyang Liu, Weijun Dong, Yingdong Hu, Chuan Wen, Zhao-Heng Yin, Chongjie Zhang, Yang Gao
Reinforcement Learning Imitation Learning Inverse Reinforcement Learning Feature Imitation Task Scheduling Robotic Agent Observation Centric

October 5, 2023

A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling
Amjad Yousef Majid, Eduard Marin
Deep Reinforcement Learning Multi Agent Reinforcement Learning Task Scheduling Serverless Computing Resource Scaling

October 3, 2023

Efficient Online Scheduling and Routing for Automated Guided Vehicles In Loop-Based Graphs
Louis Stubbe, Jens Goemaere, Jan Goedgebeur
Task Scheduling Routing Problem Scheduling Policy Guided Vehicle Single Loop

September 28, 2023

Diverse Target and Contribution Scheduling for Domain Generalization
Shaocong Long, Qianyu Zhou, Chenhao Ying, Lizhuang Ma, Yuan Luo
Strong Generalization Domain Generalization Task Scheduling Source Domain Gradient Conflict Diverse Target Target Supervision

September 18, 2023

A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm Algorithm
Hossein Jamali, Ponkoj Chandra Shill, David Feil-Seifer, Frederick C. Harris,, Sergiu M. Dascalu
Particle Swarm Optimization Task Scheduling Cloud Based Higher Order Responsibility Scheduling Algorithm Salp Swarm

September 17, 2023

A Survey on Congestion Control and Scheduling for Multipath TCP: Machine Learning vs Classical Approaches
Maisha Maliha, Golnaz Habibi, Mohammed Atiquzzaman
Machine Learning Task Scheduling Current Method Congestion Control

Task Scheduling

Papers

Boosting Column Generation with Graph Neural Networks for Joint Rider Trip Planning and Crew Shift Scheduling

On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)

A BDI Agent-Based Task Scheduling Framework for Cloud Computing

A Constraint Programming Model for Scheduling the Unloading of Trains in Ports: Extended

Reasoning with random sets: An agenda for the future

An appointment with Reproducing Kernel Hilbert Space generated by Generalized Gaussian RBF as $L^2-$measure

Multi-Agent Reinforcement Learning for Multi-Cell Spectrum and Power Allocation

Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service

Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning

Multi-defender Security Games with Schedules

Scheduling and Communication Schemes for Decentralized Federated Learning

Scheduling Distributed Flexible Assembly Lines using Safe Reinforcement Learning with Soft Shielding

Data-driven project planning: An integrated network learning and constraint relaxation approach in favor of scheduling

Scrap Your Schedules with PopDescent

Imitation Learning from Observation with Automatic Discount Scheduling

A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling

Efficient Online Scheduling and Routing for Automated Guided Vehicles In Loop-Based Graphs

Diverse Target and Contribution Scheduling for Domain Generalization

A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm Algorithm

A Survey on Congestion Control and Scheduling for Multipath TCP: Machine Learning vs Classical Approaches