Machine Learning for industrial applications: A comprehensive literature review
Abstract Machine Learning (ML) is a branch of artificial intelligence that studies algorithms
able to learn autonomously, directly from the input data. Over the last decade, ML …
able to learn autonomously, directly from the input data. Over the last decade, ML …
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 …
Dynamic scheduling of manufacturing systems using machine learning: An updated review
A common way of dynamically scheduling jobs in a manufacturing system is by
implementing dispatching rules. The issues with this method are that the performance of …
implementing dispatching rules. The issues with this method are that the performance of …
Real-time production scheduling in the Industry-4.0 context: Addressing uncertainties in job arrivals and machine breakdowns
The utilization of real-time information in production scheduling decisions becomes possible
with the help of new developments in Information Technology and Industrial Informatics …
with the help of new developments in Information Technology and Industrial Informatics …
Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments
Firms currently operate in highly competitive scenarios, where the environmental conditions
evolve over time. Many factors intervene simultaneously and their hard-to-interpret …
evolve over time. Many factors intervene simultaneously and their hard-to-interpret …
Dynamic job-shop scheduling problems using graph neural network and deep reinforcement learning
CL Liu, TH Huang - IEEE Transactions on Systems, Man, and …, 2023 - ieeexplore.ieee.org
The job-shop scheduling problem (JSSP) is one of the best-known combinatorial
optimization problems and is also an essential task in various sectors. In most real-world …
optimization problems and is also an essential task in various sectors. In most real-world …
Learning-based scheduling of flexible manufacturing systems using ensemble methods
Dispatching rules are commonly applied to schedule jobs in Flexible Manufacturing Systems
(FMSs). However, the suitability of these rules relies heavily on the state of the system; …
(FMSs). However, the suitability of these rules relies heavily on the state of the system; …
A deep multi-agent reinforcement learning approach to solve dynamic job shop scheduling problem
Manufacturing industry is experiencing a revolution in the creation and utilization of data, the
abundance of industrial data creates a need for data-driven techniques to implement real …
abundance of industrial data creates a need for data-driven techniques to implement real …
Automatic programming via iterated local search for dynamic job shop scheduling
Dispatching rules have been commonly used in practice for making sequencing and
scheduling decisions. Due to specific characteristics of each manufacturing system, there is …
scheduling decisions. Due to specific characteristics of each manufacturing system, there is …
Deep learning-based dynamic scheduling for semiconductor manufacturing with high uncertainty of automated material handling system capability
Recently, the transportation capability of the automated material handling system (AMHS)
has emerged as a major barrier to the semiconductor fabrication facility (FAB), because it …
has emerged as a major barrier to the semiconductor fabrication facility (FAB), because it …