A reinforcement learning based decision support system in textile manufacturing process

This paper introduced a reinforcement learning based decision support system in textile manufacturing process. A solution optimization problem of color fading ozonation is discussed and set up as a Markov Decision Process (MDP) in terms of tuple {S, A, P, R}. Q-learning is used to train an agent in the interaction with the setup environment by accumulating the reward R. According to the application result, it is found that the proposed MDP model has well expressed the optimization problem of textile manufacturing process discussed in this paper, therefore the use of reinforcement learning to support decision making in this sector is conducted and proven that is applicable with promising prospects

Modeling Color Fading Ozonation of Textile Using Artificial Intelligence

International audienceTextile products with faded effect achieved via ozonation are increasingly popular recently. In this study, the effect of ozonation in terms of pH, temperature, water pickup , time and applied colors on the color fading performance of reactive-dyed cotton are modeled using Extreme Learning Machine (ELM), Support Vector Regression (SVR) and Random Forest Regression (RF) respectively. It is found that RF and SVR perform better than ELM in this issue, but SVR is more recommended to be sued in the real application due to its balance predicting performance and less training time

A Deep Reinforcement Learning Based Multi-Criteria Decision Support System for Textile Manufacturing Process Optimization

Textile manufacturing is a typical traditional industry involving high complexity in interconnected processes with limited capacity on the application of modern technologies. Decision-making in this domain generally takes multiple criteria into consideration, which usually arouses more complexity. To address this issue, the present paper proposes a decision support system that combines the intelligent data-based random forest (RF) models and a human knowledge based analytical hierarchical process (AHP) multi-criteria structure in accordance to the objective and the subjective factors of the textile manufacturing process. More importantly, the textile manufacturing process is described as the Markov decision process (MDP) paradigm, and a deep reinforcement learning scheme, the Deep Q-networks (DQN), is employed to optimize it. The effectiveness of this system has been validated in a case study of optimizing a textile ozonation process, showing that it can better master the challenging decision-making tasks in textile manufacturing processes.Comment: arXiv admin note: text overlap with arXiv:2012.0110

Identification of Hyper-Methylated Tumor Suppressor Genes-Based Diagnostic Panel for Esophageal Squamous Cell Carcinoma (ESCC) in a Chinese Han Population

DNA methylation-based biomarkers were suggested to be promising for early cancer diagnosis. However, DNA methylation-based biomarkers for esophageal squamous cell carcinoma (ESCC), especially in Chinese Han populations have not been identified and evaluated quantitatively. Candidate tumor suppressor genes (N = 65) were selected through literature searching and four public high-throughput DNA methylation microarray datasets including 136 samples totally were collected for initial confirmation. Targeted bisulfite sequencing was applied in an independent cohort of 94 pairs of ESCC and normal tissues from a Chinese Han population for eventual validation. We applied nine different classification algorithms for the prediction to evaluate to the prediction performance. ADHFE1, EOMES, SALL1 and TFPI2 were identified and validated in the ESCC samples from a Chinese Han population. All four candidate regions were validated to be significantly hyper-methylated in ESCC samples through Wilcoxon rank-sum test (ADHFE1, P = 1.7 × 10-3; EOMES, P = 2.9 × 10-9; SALL1, P = 3.9 × 10-7; TFPI2, p = 3.4 × 10-6). Logistic regression based prediction model shown a moderately ESCC classification performance (Sensitivity = 66%, Specificity = 87%, AUC = 0.81). Moreover, advanced classification method had better performances (random forest and naive Bayes). Interestingly, the diagnostic performance could be improved in non-alcohol use subgroup (AUC = 0.84). In conclusion, our data demonstrate the methylation panel of ADHFE1, EOMES, SALL1 and TFPI2 could be an effective methylation-based diagnostic assay for ESCC

Modeling and optimization of textile manufacturing process using intelligent techniques

La fabrication textile joue un rôle important dans l'économie mondiale. Face à une concurrence mondiale croissante, les entreprises textiles tentent de promouvoir la flexibilité de fabrication en s’appuyant sur le concept de fabrication intelligente issu de l'industrie 4.0. Ainsi, le futur développement des processus de production textile reposera de plus en plus sur un cycle de fabrication plus court et une qualité supérieure. Cependant, les relations complexes entre les paramètres provenant des nombreux procédés textiles et la grande variété de produits rend le contrôle et l’optimisation de la fabrication très difficile. Afin de surmonter ces problèmes, des techniques intelligentes de modélisation des processus et d’apprentissage à partir de données expérimentales sont utilisées dans cette thèse pour optimiser la fabrication textile.Dans cette thèse une étude approfondie de la littérature est menée sur les travaux précédents concernant la modélisation et l'optimisation du processus de fabrication textile à l'aide de techniques intelligentes. La synthèse de ces travaux, des avantages et inconvénients des différentes techniques, ont fourni une base théorique et une direction de recherche sur la méthodologie à suivre. Trois sous-études ont ainsi été développées. La première étude de cas spécifique porte sur la modélisation des processus d'ozonation des textiles à l'aide de réseaux neuronaux de type “extreme learning machine” (ELM), de régression par machines à vecteurs “support vector regression” (SVR) et de forêt d’arbres décisionnels “random forest” (RF). Les modèles SVR et RF ont montré les meilleures aptitudes à modéliser les interrelations incertaines des variables dans le processus textile avec un nombre réduit de données d'apprentissage, mais nécessite des temps d’exécution plus importants. Sur la base des modèles RF établis, un nouveau système d'aide à la décision multicritères a ensuite été développé, dans une deuxième étude, pour l'optimisation textile en combinaison avec une méthode de hiérarchie multicritère, “analytical hierarchy process” (AHP), et de l'algorithme Deep Q-networks (DQN). Le processus textile est alors formalisé comme un processus de décision markovien, “Markov decision process” (MDP). Le résultat obtenu par ce modèle montre qu'il est possible de contrôler les relations décisionnelles complexes qui régissent le processus de fabrication textile. Dans la troisième étude, afin de mieux répondre à la complexité croissante de ce problème en milieu industriel, le système développé est intégrée dans un système multi-agents pour l'optimisation multi-objectifs du processus de fabrication textile. Les différents systèmes proposés permettent d'optimiser le processus de fabrication textile et aider les industries textiles à converger vers une fabrication intelligente pour maintenir leur compétitivité.Textile manufacturing plays an important role in the world economy. While the globally increasing competition is stressing the textile companies to promote the manufacturing flexibility, as a trend of intelligent manufacturing in Industry 4.0, the future development of the textile manufacturing process will increasingly rely on shorter cycle and higher quality. However, the complicated intricate relationship between the large-scale parameter variables from a variety of textile processes makes it seem incredibly difficult. In order to overcome these issues, intelligent techniques are employed in this thesis to promote textile manufacturing from the process modeling and optimization.In this Ph.D. research, a thorough investigation and literature review regarding the previous studies on modeling and optimization of the textile manufacturing process using intelligent techniques. A series of the summarizations were determined in pros and cons, which provided a theoretical foundation and research direction for the subsequent studies. Three sub-studies thus were developed: A specific case study on textile ozonation process modeling using extreme learning machine (ELM), support vector regression (SVR) and random forest (RF) was developed, where the SVR models and RF models were found that both can well address the uncertain interrelationships of variables in the textile process modeling with less training data, but their requirement on training time is different. On the basis of the established RF models, a novel multi-criteria decision support system was then developed for textile optimization with the collaboration of the analytical hierarchy process (AHP) and the Deep Q-networks (DQN) algorithm, where the textile process is formulated as the Markov decision process (MDP) paradigm, and the application result showed that it can master the challenging decision-making tasks in the textile manufacturing process. To better address the growing complexity in this issue, the application of this developed system is further integrated into a multi-agent system for multi-objective optimization in the textile manufacturing process. The developed systems can optimize the textile process and help companies maintain competence in the trend of intelligent manufacturing in the textile industry

Cost Optimal Production-Scheduling Model Based on VNS-NSGA-II Hybrid Algorithm—Study on Tissue Paper Mill

With the development of the customization concept, small-batch and multi-variety production will become one of the major production modes, especially for fast-moving consumer goods. However, this production mode has two issues: high production cost and the long manufacturing period. To address these issues, this study proposes a multi-objective optimization model for the flexible flow-shop to optimize the production scheduling, which would maximize the production efficiency by minimizing the production cost and makespan. The model is designed based on hybrid algorithms, which combine a fast non-dominated genetic algorithm (NSGA-II) and a variable neighborhood search algorithm (VNS). In this model, NSGA-II is the major algorithm to calculate the optimal solutions. VNS is to improve the quality of the solution obtained by NSGA-II. The model is verified by an example of a real-world typical FFS, a tissue papermaking mill. The results show that the scheduling model can reduce production costs by 4.2% and makespan by 6.8% compared with manual scheduling. The hybrid VNS-NSGA-II model also shows better performance than NSGA-II, both in production cost and makespan. Hybrid algorithms are a good solution for multi-objective optimization issues in flexible flow-shop production scheduling

Modeling of textile manufacturing processes using intelligent techniques: a review

International audienceAs the need for quickly exploring a textile manufacturing process is increasingly costly along with the complexity in the process. The development of manufacturing process modeling has attracted growing attention from the textile industry. More and more researchers shift their attention from classic methods to the intelligent techniques for process modeling as the traditional ones can hardly depict the intricate relationships of numerous process factors and performances. In this study, the literature investigating the process modeling of textile manufacturing is systematically reviewed. The structure of this paper is in line with the procedure of textile processes from yarn to fabrics, and then to garments. The analysis and discussion of the previous studies are conducted on different applications in different processes. The factors and performance properties considered in process modeling are collected in comparison. In terms of inputs' relative importance, feature selection, modeling techniques, data distribution, and performance estimations, the considerations of the previous studies are analyzed and summarized. It is also concluded the limitations, challenges, and future perspectives in this issue on the basis of the summaries of more than 130 related articles from the point of views of textile engineering and artificial intelligence

Multi-Objective Optimization of the Textile Manufacturing Process Using Deep-Q-Network Based Multi-Agent Reinforcement Learning

Multi-objective optimization of the textile manufacturing process is an increasing challenge because of the growing complexity involved in the development of the textile industry. The use of intelligent techniques has been often discussed in this domain, although a significant improvement from certain successful applications has been reported, the traditional methods failed to work with high-as well as human intervention. Upon which, this paper proposed a multi-agent reinforcement learning (MARL) framework to transform the optimization process into a stochastic game and introduced the deep Q-networks algorithm to train the multiple agents. A utilitarian selection mechanism was employed in the stochastic game, which (-greedy policy) in each state to avoid the interruption of multiple equilibria and achieve the correlated equilibrium optimal solutions of the optimizing process. The case study result reflects that the proposed MARL system is possible to achieve the optimal solutions for the textile ozonation process and it performs better than the traditional approaches