☆ 4.6 Article

Multi-Agent Reinforcement Learning for Real-Time Dynamic Production Scheduling in a Robot Assembly Cell

IEEE ROBOTICS AND AUTOMATION LETTERS (2022)

Related references

Note: Only part of the references are listed.

Article Computer Science, Artificial Intelligence

Heterogeneous graph attention networks for scalable multi-robot scheduling with temporospatial constraints

Zheyuan Wang et al.

Summary: In this paper, a novel heterogeneous graph attention network model called ScheduleNet is proposed to learn scheduling policies that overcome the limitations of conventional methods. By introducing robot- and proximity-specific nodes into the simple temporal network, a nonparametric heterogeneous graph structure is obtained. The model is shown to be end-to-end trainable on small-scale problems and generalizes to large, unseen problems, outperforming existing state-of-the-art methods in various testing scenarios involving both homogeneous and heterogeneous robot teams.

AUTONOMOUS ROBOTS (2022)

Add to Collection

Article Automation & Control Systems

Real-Time Scheduling for Dynamic Partial-No-Wait Multiobjective Flexible Job Shop by Deep Reinforcement Learning

Shu Luo et al.

Summary: In this study, a hierarchical multiagent deep reinforcement learning (DRL)-based real-time scheduling method named HMAPPO is proposed to address the dynamic partial-no-wait multiobjective flexible job shop scheduling problem. The method consists of objective agent, job agent, and machine agent, with various job selection rules and machine assignment rules designed to achieve temporary objectives at each rescheduling point. Extensive numerical experiments have confirmed the effectiveness and superiority of HMAPPO compared to other known dynamic scheduling methods.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2022)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Solving job scheduling problems in a resource preemption environment with multi-agent reinforcement learning

Xiaohan Wang et al.

Summary: This paper proposes a multi-agent reinforcement learning algorithm to solve job scheduling problems in a resource preemption environment. By modeling the resource preemption environment as a decentralized partially observable Markov decision process and constructing a multi-agent scheduling architecture, the decision-making policy of each agent and the cooperation between job agents are learned. The experimental results demonstrate the superiority of the proposed method in terms of total makespan, training stability, and model generalization, compared to traditional rule-based methods and distributed-agent reinforcement learning methods.

ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2022)

Add to Collection

Article Computer Science, Interdisciplinary Applications

A hybrid multi-objective approach for real-time flexible production scheduling and rescheduling under dynamic environment in Industry 4.0 context

Sicheng Zhang et al.

Summary: This study aims to address the production scheduling problem of complex multi-level products in a smart manufacturing system, introducing a hybrid MPGA-CP approach. Compared to alternative methods, it demonstrates outstanding efficiency and performance in solving medium-to-large scale problems.

COMPUTERS & OPERATIONS RESEARCH (2021)

Add to Collection

Review Engineering, Industrial

Self-organizing manufacturing network: A paradigm towards smart manufacturing in mass personalization

Zhaojun Qin et al.

Summary: Mass personalization requires flexible manufacturing systems to meet changing demands, but current systems cannot adapt to dynamic production environments. To achieve flexible, autonomous, and error-tolerant production, a manufacturing system capable of self-optimization is needed. The concept of Self-Organizing Manufacturing Network is proposed as the next-generation manufacturing automation technology to achieve mass personalization.

JOURNAL OF MANUFACTURING SYSTEMS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning

Shu Luo

APPLIED SOFT COMPUTING (2020)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0

Hao Hu et al.

COMPUTERS & INDUSTRIAL ENGINEERING (2020)

Add to Collection

Article Robotics

Learning Scheduling Policies for Multi-Robot Coordination With Graph Attention Networks

Zheyuan Wang et al.

IEEE ROBOTICS AND AUTOMATION LETTERS (2020)

Add to Collection

Review Engineering, Industrial

Smart manufacturing process and system automation - A critical review of the standards and envisioned scenarios

Yuqian Lu et al.

JOURNAL OF MANUFACTURING SYSTEMS (2020)

Add to Collection

Proceedings Paper Automation & Control Systems

A Deep Reinforcement Learning Approach to the Flexible Flowshop Scheduling Problem with Makespan Minimization

Jialin Zhu et al.

PROCEEDINGS OF 2020 IEEE 9TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS'20) (2020)

Add to Collection

Article Automation & Control Systems

A Reinforcement Learning Approach to Robust Scheduling of Semiconductor Manufacturing Facilities

In-Beom Park et al.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2020)

Add to Collection

Article Computer Science, Information Systems

A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment

Yeou-Ren Shiue et al.

IEEE ACCESS (2020)

Add to Collection

Article Automation & Control Systems

Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network

Chun-Cheng Lin et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

Add to Collection

Proceedings Paper Engineering, Electrical & Electronic

An Improved Genetic Algorithm for Flexible Job Shop Scheduling Problem

Jiang Liangxiao et al.

2015 2ND INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING ICISCE 2015 (2015)

Add to Collection

Article Computer Science, Artificial Intelligence

An effective genetic algorithm for the flexible job-shop scheduling problem

Guohui Zhang et al.

EXPERT SYSTEMS WITH APPLICATIONS (2011)

Add to Collection

Article Computer Science, Interdisciplinary Applications

A genetic algorithm for the Flexible Job-shop Scheduling Problem

F. Pezzella et al.

COMPUTERS & OPERATIONS RESEARCH (2008)

Add to Collection

Review Computer Science, Artificial Intelligence

A comprehensive survey of multiagent reinforcement learning

Lucian Busoniu et al.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS (2008)

Add to Collection

© Peeref 2019-2024. All rights reserved.