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Applied Sciences
Special Issues
Learning Based Methods for Industrial Applications
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Learning Based Methods for Industrial Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (10 January 2022) | Viewed by 2851

Special Issue Editors

Prof. Dr. Xianpeng Wang

E-Mail Website
Guest Editor
College of Information Science and Engineering, Northeastern University, Shenyang 110006, China
Interests: industrial big data analysis; machine learning; industrial image deep learning; evolutionary computation; intelligent optimization algorithm; production process modeling and operation optimization; production scheduling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Danyu Bai

E-Mail Website
Guest Editor
School of Maritime Economics & Management, Dalian Maritime University, Dalian 116026, China
Interests: production scheduling; intelligent algorithms; smart manufacturing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Peng Liu

E-Mail Website
Guest Editor
School of Management, Shenyang University of Technology, Shenyang 110870, China
Interests: production scheduling; intelligent algorithms; smart manufacturing

Special Issue Information

Dear Colleagues,

Industrial big data is a general term for the data sets related to the industrial manufacturing process, which is the core of the industrial Internet and an important foundation for the development of industrial intelligence. In recent years, mining industrial big data based on machine learning methods to achieve optimization of industrial production and management has received more and more attention. As a result, many successful applications have been achieved in data-driven production process modeling, production process operation optimization, production scheduling, etc.

The aim of this Special Issue is to attract world-leading researchers in the area of learning-based methods for industrial applications in an effort to highlight the latest exciting developments, discuss the new methods of data analysitcs and optimization, and promote specific applications of learning-based methods in various industries. The accepted contributions will include learning-based production process modeling, learning-based product quality prediction, deep-learning-based industrial image analytics, learning-based production process fault diagnosis, learning-based production scheduling and management, etc.

Prof. Dr. Xianpeng Wang
Prof. Dr. Danyu Bai
Prof. Dr. Peng Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

22 pages, 3641 KiB  
Article
Minimizing the Late Work of the Flow Shop Scheduling Problem with a Deep Reinforcement Learning Based Approach
by Zhuoran Dong, Tao Ren, Jiacheng Weng, Fang Qi and Xinyue Wang
Appl. Sci. 2022, 12(5), 2366; https://doi.org/10.3390/app12052366 - 24 Feb 2022
Cited by 5 | Viewed by 2071
Abstract
In the field of industrial manufacturing, assembly line production is the most common production process that can be modeled as a permutation flow shop scheduling problem (PFSP). Minimizing the late work criteria (tasks remaining after due dates arrive) of production planning can effectively [...] Read more.
In the field of industrial manufacturing, assembly line production is the most common production process that can be modeled as a permutation flow shop scheduling problem (PFSP). Minimizing the late work criteria (tasks remaining after due dates arrive) of production planning can effectively reduce production costs and allow for faster product delivery. In this article, a novel learning-based approach is proposed to minimize the late work of the PFSP using deep reinforcement learning (DRL) and graph isomorphism network (GIN), which is an innovative combination of the field of combinatorial optimization and deep learning. The PFSPs are the well-known permutation flow shop problem and each job comes with a release date constraint. In this work, the PFSP is defined as a Markov decision process (MDP) that can be solved by reinforcement learning (RL). A complete graph is introduced for describing the PFSP instance. The proposed policy network combines the graph representation of PFSP and the sequence information of jobs to predict the distribution of candidate jobs. The policy network will be invoked multiple times until a complete sequence is obtained. In order to further improve the quality of the solution obtained by reinforcement learning, an improved iterative greedy (IG) algorithm is proposed to search the solution locally. The experimental results show that the proposed RL and the combined method of RL+IG can obtain better solutions than other excellent heuristic and meta-heuristic algorithms in a short time. Full article
(This article belongs to the Special Issue Learning Based Methods for Industrial Applications)
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