A review on learning to solve combinatorial optimisation problems in manufacturing
An efficient manufacturing system is key to maintaining a healthy economy today. With the
rapid development of science and technology and the progress of human society, the …
rapid development of science and technology and the progress of human society, the …
A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem
This paper presents an end-to-end deep reinforcement framework to automatically learn a
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
Beyond games: a systematic review of neural Monte Carlo tree search applications
The advent of AlphaGo and its successors marked the beginning of a new paradigm in
playing games using artificial intelligence. This was achieved by combining Monte Carlo …
playing games using artificial intelligence. This was achieved by combining Monte Carlo …
Logistics-involved task scheduling in cloud manufacturing with offline deep reinforcement learning
As an application of industrial information integration engineering (IIIE) in manufacturing,
cloud manufacturing (CMfg) integrates enterprises' manufacturing information and provides …
cloud manufacturing (CMfg) integrates enterprises' manufacturing information and provides …
Emergence of collective intelligence in industrial cyber-physical-social systems for collaborative task allocation and defect detection
ILD Makanda, P Jiang, M Yang, H Shi - Computers in Industry, 2023 - Elsevier
The manufacturing industry is facing the challenge of meeting the growing demand for
personalized products, which requires enhanced agility, flexibility, reconfigurability, and …
personalized products, which requires enhanced agility, flexibility, reconfigurability, and …
An end-to-end reinforcement learning approach for job-shop scheduling problems based on constraint programming
Constraint Programming (CP) is a declarative programming paradigm that allows for
modeling and solving combinatorial optimization problems, such as the Job-Shop …
modeling and solving combinatorial optimization problems, such as the Job-Shop …
A parallel deep reinforcement learning framework for controlling industrial assembly lines
Decision-making in a complex, dynamic, interconnected, and data-intensive industrial
environment can be improved with the assistance of machine-learning techniques. In this …
environment can be improved with the assistance of machine-learning techniques. In this …
强化学习求解组合最优化问题的研究综述.
王扬， 陈智斌， 吴兆蕊， 高远 - Journal of Frontiers of …, 2022 - search.ebscohost.com
组合最优化问题(COP) 的求解方法已经渗透到人工智能, 运筹学等众多领域.
随着数据规模的不断增大, 问题更新速度的变快, 运用传统方法求解COP 问题在速度, 精度 …
随着数据规模的不断增大, 问题更新速度的变快, 运用传统方法求解COP 问题在速度, 精度 …
A reinforcement learning approach for scheduling problems with improved generalization through order swapping
D Vivekanandan, S Wirth, P Karlbauer… - Machine Learning and …, 2023 - mdpi.com
The scheduling of production resources (such as associating jobs to machines) plays a vital
role for the manufacturing industry not only for saving energy, but also for increasing the …
role for the manufacturing industry not only for saving energy, but also for increasing the …
深度强化学习求解车辆路径问题的研究综述.
杨笑笑， 柯琳， 陈智斌 - Journal of Computer Engineering & …, 2023 - search.ebscohost.com
车辆路径问题(VRP) 是组合优化问题中经典的NP 难问题, 广泛应用于交通, 物流等领域,
随着问题规模和动态因素的增多, 传统算法很难快速, 智能地求解复杂的VRP 问题 …
随着问题规模和动态因素的增多, 传统算法很难快速, 智能地求解复杂的VRP 问题 …