Inicio  /  Applied Sciences  /  Vol: 12 Par: 18 (2022)  /  Artículo
ARTÍCULO
TITULO

Deep Reinforcement Learning Approach for Material Scheduling Considering High-Dimensional Environment of Hybrid Flow-Shop Problem

Chang-Bae Gil and Jee-Hyong Lee    

Resumen

Manufacturing sites encounter various scheduling problems, which must be dealt with to efficiently manufacture products and reduce costs. With the development of smart factory technology, many elements at manufacturing sites have become unmanned and more complex. Moreover, owing to the mixing of several processes in one production line, the need for efficient scheduling of materials has emerged. The aim of this study is to solve the material scheduling problem of many machines in a hybrid flow-shop environment using deep reinforcement learning. Most previous work has ignored some conditions, which were critical for solving practical problems. Such critical conditions make the scheduling more complex and difficult to solve. They expand the size of the state and large action space and make learning in an environment with many machines problematic. In this study, a reinforcement learning approach was developed considering practical factors such as the processing time and material transfer to solve realistic manufacturing scheduling problems. Additionally, a method to simplify the high-dimensional environmental space at manufacturing sites for efficient learning was established to solve the problem of learning in a high-dimensional space. Through experiments, we showed that our approach could optimally schedule material scheduling in multi-process lines, which contributes to realistic manufacturing intelligence.

 Artículos similares

       
 
Zheng Li, Xinkai Chen, Jiaqing Fu, Ning Xie and Tingting Zhao    
With the development of electronic game technology, the content of electronic games presents a larger number of units, richer unit attributes, more complex game mechanisms, and more diverse team strategies. Multi-agent deep reinforcement learning shines ... ver más
Revista: Algorithms

 
Bohdan Petryshyn, Serhii Postupaiev, Soufiane Ben Bari and Armantas Ostreika    
The development of autonomous driving models through reinforcement learning has gained significant traction. However, developing obstacle avoidance systems remains a challenge. Specifically, optimising path completion times while navigating obstacles is ... ver más
Revista: Information

 
Bowen Xing, Xiao Wang and Zhenchong Liu    
The path planning strategy of deep-sea mining vehicles is an important factor affecting the efficiency of deep-sea mining missions. However, the current traditional path planning algorithms suffer from hose entanglement problems and small coverage in the... ver más

 
Eyad K. Sayhood, Nisreen S. Mohammed, Salam J. Hilo and Salih S. Salih    
This paper presents comprehensive empirical equations to predict the shear strength capacity of reinforced concrete deep beams, with a focus on improving the accuracy of existing codes. Analyzing 198 deep beams imported from 15 existing investigations, t... ver más
Revista: Infrastructures

 
Juyao Wei, Zhenggang Lu, Zheng Yin and Zhipeng Jing    
This paper presents a novel data-driven multiagent reinforcement learning (MARL) controller for enhancing the running stability of independently rotating wheels (IRW) and reducing wheel?rail wear. We base our active guidance controller on the multiagent ... ver más
Revista: Applied Sciences