A review of reinforcement learning based intelligent optimization for manufacturing scheduling
As the critical component of manufacturing systems, production scheduling aims to optimize
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
objectives in terms of profit, efficiency, and energy consumption by reasonably determining …
Deep reinforcement learning in production systems: a systematic literature review
Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …
challenges for production systems. These not only have to cope with an increased product …
Deep reinforcement learning for dynamic scheduling of a flexible job shop
The ability to handle unpredictable dynamic events is becoming more important in pursuing
agile and flexible production scheduling. At the same time, the cyber-physical convergence …
agile and flexible production scheduling. At the same time, the cyber-physical convergence …
A machine learning approach for energy-efficient intelligent transportation scheduling problem in a real-world dynamic circumstances
This paper provides a novel intelligent scheduling strategy for a real-world transportation
dynamic scheduling case from an engine workshop of general motor company (GMEW) …
dynamic scheduling case from an engine workshop of general motor company (GMEW) …
Advances and opportunities in machine learning for process data analytics
In this paper we introduce the current thrust of development in machine learning and
artificial intelligence, fueled by advances in statistical learning theory over the last 20 years …
artificial intelligence, fueled by advances in statistical learning theory over the last 20 years …
A reinforcement learning approach for flexible job shop scheduling problem with crane transportation and setup times
Y Du, J Li, C Li, P Duan - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Flexible job shop scheduling problem (FJSP) has attracted research interests as it can
significantly improve the energy, cost, and time efficiency of production. As one type of …
significantly improve the energy, cost, and time efficiency of production. As one type of …
[HTML][HTML] A multi-stage stochastic programming model for the unit commitment of conventional and virtual power plants bidding in the day-ahead and ancillary services …
As more uncontrollable renewable energy sources are present in the power generation
portfolio, the need of more detailed and reliable tools for the optimal operation of energy …
portfolio, the need of more detailed and reliable tools for the optimal operation of energy …
Industrial data science–a review of machine learning applications for chemical and process industries
In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to
start with examples that are irrelevant to process engineers (eg classification of images …
start with examples that are irrelevant to process engineers (eg classification of images …
Reinforcement learning applied to production planning and control
The objective of this paper is to examine the use and applications of reinforcement learning
(RL) techniques in the production planning and control (PPC) field addressing the following …
(RL) techniques in the production planning and control (PPC) field addressing the following …
Deep reinforcement learning based optimization algorithm for permutation flow-shop scheduling
As a new analogy paradigm of human learning process, reinforcement learning (RL) has
become an emerging topic in computational intelligence (CI). The synergy between the RL …
become an emerging topic in computational intelligence (CI). The synergy between the RL …