Cloud manufacturing promotes the transformation of intelligence for the traditional manufacturing mode. In a cloud manufacturing environment, the task scheduling plays an important role.However, as the number of problem instances increases, the solution quality and computation time always go against. Existing task scheduling algorithms can get local optimal solutions with the high computational cost, especially for large problem instances. To tackle this problem, a task scheduling algorithm based on a deep reinforcement learning architecture (RLTS) is proposed to dynamically schedule tasks with precedence relationship to cloud servers to minimize the task execution time. Meanwhile, the Deep-Q-Network, as a kind of deep reinforcement learning algorithms, is employed to consider the problem of complexity and high dimension.In the simulation, the performance of the proposed algorithm is compared with other four heuristic algorithms. The experimental results show that RLTS can be effective to solve the task scheduling in a cloud manufacturing environment. KEYWORDS cloud manufacturing, Deep-Q-Network, deep reinforcement learning, task scheduling INTRODUCTIONThe traditional manufacturing is transforming into the intelligent manufacturing due to the development of information and technology.Cloud manufacturing, 1 as an advanced manufacturing paradigm, is a manifestation of the concept of ''Manufacturing as a Service''. The cloud manufacturing platform aggregates a collection of distributed servers to execute the demand of users. However, it still faces many challenges in some aspects, such as security, sustainability, reliability, and optimized management. Task scheduling optimization between the server resources and the user's demand tasks is the critical factors restricting the development of cloud manufacturing. [2][3][4] In a large-scale distributed cloud manufacturing environment, unreasonable task scheduling will cause the problems of reducing cloud server resource utilization, degrading system performance, and increasing operating costs. Therefore, how to schedule tasks reasonably and effectively has always been the focus in both industrial and academic communities.The task scheduling in this work is simply described. The user submits an application consisting of tasks with the precedence constraint to the cloud platform. When it receives the application, servers in the platform are allocated according to the demand of each task to finish the user's request. It is critical for the platform to efficiently leverage its servers to finish the timely and cost effective delivery to users. SchedulingConcurrency Computat Pract Exper. 2020;32:e5654. wileyonlinelibrary.com/journal/cpe
To find out what other people think has been an essential part of information-gathering behaviors. And in the case of movies, the movie reviews can provide an intricate insight into the movie and can help decide whether it is worth spending time on. However, with the growing amount of data in reviews, it is quite prudent to automate the process, saving on time. Sentiment analysis is an important field of study in machine learning that focuses on extracting information of subject from the textual reviews. The area of analysis of sentiments is related closely to natural language processing and text mining. It can successfully be used to determine the attitude of the reviewer in regard to various topics or the overall polarity of the review. In the case of movie reviews, along with giving a rating in numeric to a movie, they can enlighten us on the favorableness or the opposite of a movie quantitatively; a collection of those then gives us a comprehensive qualitative insight on different facets of the movie. Opinion mining from movie reviews can be challenging due to the fact that human language is rather complex, leading to situations where a positive word has a negative connotation and vice versa. In this study, the task of opinion mining from movie reviews has been achieved with the use of neural networks trained on the "Movie Review Database" issued by Stanford, in conjunction with two big lists of positive and negative words. The trained network managed to achieve a final accuracy of 91%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations –citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.