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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 …
[HTML][HTML] Deep reinforcement learning-based dynamic scheduling for resilient and sustainable manufacturing: A systematic review
Dynamic scheduling plays a pivotal role in smart manufacturing by enabling real-time
adjustments to production schedules, thereby enhancing system resilience and promoting …
adjustments to production schedules, thereby enhancing system resilience and promoting …
Towards live decision-making for service-based production: Integrated process planning and scheduling with Digital Twins and Deep-Q-Learning
Production flow is becoming increasingly complex since manufacturers must react quickly to
changing markets demands and diverse customer requirements. In order to ensure …
changing markets demands and diverse customer requirements. In order to ensure …
[HTML][HTML] An end-to-end deep learning method for dynamic job shop scheduling problem
S Chen, Z Huang, H Guo - Machines, 2022 - mdpi.com
Job shop scheduling problem (JSSP) is essential in the production, which can significantly
improve production efficiency. Dynamic events such as machine breakdown and job rework …
improve production efficiency. Dynamic events such as machine breakdown and job rework …
Fabricatio-rl: a reinforcement learning simulation framework for production scheduling
Production scheduling is the task of assigning job operations to processing resources such
that a target goal is optimized. constraints on job structure and resource capabilities …
that a target goal is optimized. constraints on job structure and resource capabilities …
Design of a Machine Learning-based Decision Support System for Product Scheduling on Non Identical Parallel Machines
Production planning in supply chain management faces considerable challenges due to the
dynamics and unpredictability of the production environment. Decision support systems …
dynamics and unpredictability of the production environment. Decision support systems …
[HTML][HTML] A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective
In this study, a systematic review on production scheduling based on reinforcement learning
(RL) techniques using especially bibliometric analysis has been carried out. The aim of this …
(RL) techniques using especially bibliometric analysis has been carried out. The aim of this …
A blueprint description of production scheduling models using asset administration shells
ABSTRACT Production Planning and Control involves combinatorial optimization problems
subject to domain-related constraints. Hence, decision-support systems are required to …
subject to domain-related constraints. Hence, decision-support systems are required to …
Modelling framework for reinforcement learning based scheduling applications
Over the last years, reinforcement learning has been extensively applied to schedule
complex and dynamic systems. There are multitudes of simulation environments and …
complex and dynamic systems. There are multitudes of simulation environments and …
Towards practicality: Navigating challenges in designing predictive-reactive scheduling
Today's agile production systems face an ever-increasing complexity due to individualized
mass production, a volatile customer demand and dynamic events which disrupt the …
mass production, a volatile customer demand and dynamic events which disrupt the …