scholarly journals Production scheduling in industrial mining complexes with incoming new information using tree search and deep reinforcement learning

2021 ◽  
pp. 107644
Author(s):  
Ashish Kumar ◽  
Roussos Dimitrakopoulos
Keyword(s):  
Reinforcement Learning ◽  
Production Scheduling ◽  
Tree Search ◽  
New Information ◽  
Industrial Mining

scholarly journals Enhanced Reinforcement Learning Method Combining One-Hot Encoding-Based Vectors for CNN-Based Alternative High-Level Decisions

2021 ◽  
Vol 11 (3) ◽  
pp. 1291
Author(s):  
Bonwoo Gu ◽  
Yunsick Sung
Keyword(s):  
Reinforcement Learning ◽  
Search Algorithm ◽  
Classification Criteria ◽  
Tree Search ◽  
Learning Method ◽  
Board Game ◽  
Ancient China ◽  
Monte Carlo Tree Search ◽  
High Level ◽  
Tree Search Algorithm

Gomoku is a two-player board game that originated in ancient China. There are various cases of developing Gomoku using artificial intelligence, such as a genetic algorithm and a tree search algorithm. Alpha-Gomoku, Gomoku AI built with Alpha-Go’s algorithm, defines all possible situations in the Gomoku board using Monte-Carlo tree search (MCTS), and minimizes the probability of learning other correct answers in the duplicated Gomoku board situation. However, in the tree search algorithm, the accuracy drops, because the classification criteria are manually set. In this paper, we propose an improved reinforcement learning-based high-level decision approach using convolutional neural networks (CNN). The proposed algorithm expresses each state as One-Hot Encoding based vectors and determines the state of the Gomoku board by combining the similar state of One-Hot Encoding based vectors. Thus, in a case where a stone that is determined by CNN has already been placed or cannot be placed, we suggest a method for selecting an alternative. We verify the proposed method of Gomoku AI in GuPyEngine, a Python-based 3D simulation platform.

scholarly journals Reinforcement Learning With Composite Rewards for Production Scheduling in a Smart Factory

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 752-766
Author(s):  
Tong Zhou ◽  
Dunbing Tang ◽  
Haihua Zhu ◽  
Liping Wang
Keyword(s):  
Reinforcement Learning ◽  
Production Scheduling ◽  
Smart Factory

scholarly journals A deep reinforcement learning approach for chemical production scheduling

2020 ◽  
Vol 141 ◽  
pp. 106982 ◽  
Author(s):  
Christian D. Hubbs ◽  
Can Li ◽  
Nikolaos V. Sahinidis ◽  
Ignacio E. Grossmann ◽  
John M. Wassick
Keyword(s):  
Reinforcement Learning ◽  
Production Scheduling ◽  
Learning Approach ◽  
Chemical Production

scholarly journals Reinforcement Learning to Create Value and Policy Functions Using Minimax Tree Search in Hex

2020 ◽  
Vol 12 (1) ◽  
pp. 63-73
Author(s):  
Kei Takada ◽  
Hiroyuki Iizuka ◽  
Masahito Yamamoto
Keyword(s):  
Reinforcement Learning ◽  
Tree Search

scholarly journals Injection Mold Production Sustainable Scheduling Using Deep Reinforcement Learning

2020 ◽  
Vol 12 (20) ◽  
pp. 8718 ◽  
Author(s):  
Seunghoon Lee ◽  
Yongju Cho ◽  
Young Hoon Lee
Keyword(s):  
Reinforcement Learning ◽  
Production Scheduling ◽  
Dynamic Environment ◽  
Total Weighted Tardiness ◽  
Injection Mold ◽  
Weighted Tardiness ◽  
Scheduling Policy ◽  
Delivery Date ◽  
Process Framework ◽  
Markov Decision

In the injection mold industry, it is important for manufacturers to satisfy the delivery date for the products that customers order. The mold products are diverse, and each product has a different manufacturing process. Owing to the nature of mold, mold manufacturing is a complex and dynamic environment. To meet the delivery date of the customers, the scheduling of mold production is important and is required to be sustainable and intelligent even in the complicated system and dynamic situation. To address this, in this paper, deep reinforcement learning (RL) is proposed for injection mold production scheduling. Before presenting the RL algorithm, a mathematical model for the mold scheduling problem is presented, and a Markov decision process framework is proposed for RL. The deep Q-network, which is an algorithm for RL, is employed to find the scheduling policy to minimize the total weighted tardiness. The results of experiments demonstrate that the proposed deep RL method outperforms the dispatching rules that are presented for minimizing the total weighted tardiness.

scholarly journals Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning

2020 ◽  
Vol 11 (40) ◽  
pp. 10959-10972
Author(s):  
Xiaoxue Wang ◽  
Yujie Qian ◽  
Hanyu Gao ◽  
Connor W. Coley ◽  
Yiming Mo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Prediction Model ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Value Network ◽  
Synthesis Routes

A new MCTS variant with a reinforcement learning value network and solvent prediction model proposes shorter synthesis routes with greener solvents.

scholarly journals Reinforcement Learning for Bio-Retrosynthesis

2019 ◽  
Author(s):  
Mathilde Koch ◽  
Thomas Duigou ◽  
Jean-Loup Faulon
Keyword(s):  
Reinforcement Learning ◽  
Metabolic Engineering ◽  
Development Process ◽  
Computational Design ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Command Line Tool ◽  
Living Organisms ◽  
Media Supplements ◽  
Engineering Projects

AbstractMetabolic engineering aims to produce chemicals of interest from living organisms, to advance towards greener chemistry. Despite efforts, the research and development process is still long and costly and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bio-retrosynthesis space using an Artificial Intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden dataset of 20 manually curated experimental pathways as well as on a larger dataset of 152 successful metabolic engineering projects. Moreover, we provide a novel feature, that suggests potential media supplements to complement the enzymatic synthesis plan.

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