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Sustainable Manufacturing Systems Using Big Data Analytics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 3367

Special Issue Editors


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Guest Editor
Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor, Malaysia
Interests: operations research; operations management; sustainable manufacturing systems; optimization; soft computing

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Guest Editor
Peter B. Gustavson School of Business, University of Victoria, P.O. Box 1700, Victoria, BC, Canada
Interests: supply chain management; healthcare systems; sustainable logistics and production management; optimization algorithms; heuristics; metaheuristics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor

Special Issue Information

Dear Colleagues,

There is a great deal of concern and interest in environmental sustainability with respect to carbon emissions, global warming, and toxic hazes nowadays. In addition to environmental pollution, the social sustainability agenda of improving the quality of human life is critical. To meet the standards of global sustainable development, simultaneous consideration of environmental and social factors, in addition to their financial costs, is critical. A sustainable design and manufacturing process for the manufacturing industry could not only reduce financial costs but also minimize carbon emissions and waste energy and maximize social factors. The large amounts of carbon emissions and energy waste from the design and manufacturing industry is a worldwide concern, and the development a sustainable design to address the financial, environmental, and social factors is a primary area of interest.

Advanced technologies and industrial informatics based on energy-efficient cloud computing, the internet of things, big data, blockchain, and knowledge management have been widely studied to facilitate the achievement of smart design, manufacturing, and remanufacturing strategies. In fact, without the support of data science and technology, achieving “smart” technologies and strategies would not be feasible.

The concept of a sustainable, intelligent manufacturing system has motivated many researchers and practitioners to explore new methods, models, and technologies for industrial applications of big data in sustainable design and manufacturing.

This Special Issue of Applied Sciences invites experts in the field to provide high-quality articles and reviews with a focus on the design and development of novel algorithms, models, technologies, and tools for the creation of a sustainable manufacturing system using big data analytics. Topics of interest include, but are not limited to:

  • Sustainable design methods and manufacturing technologies;
  • Big data analytics and knowledge management for sustainable manufacturing systems;
  • Addressing uncertainty for modeling a sustainable manufacturing system using big data analytics;
  • Sustainable design of products and remanufacturing strategies using big data analytics;
  • Engineering design attributes based on data analysis for sustainable design of products;
  • Smart systems using industrial informatics for sustainable design and manufacturing systems;
  • Proposing machine learning and artificial intelligence algorithms for the simulation of sustainable manufacturing systems;
  • Decision-making tools for the process of developing sustainable design and manufacturing systems;
  • Sustainable operations and service quality management using big data analytics;
  • Sustainable transportation and logistics for manufacturing and remanufacturing systems;
  • Data mining and knowledge management and blockchain technologies for manufacturing and remanufacturing systems;
  • Optimization algorithms, machine learning, and deep learning methods for sustainable manufacturing systems;
  • Energy-efficient manufacturing and remanufacturing systems using cloud-computing platforms;
  • Modeling and controlling manufacturing systems using big data;
  • Big data analytics and data science algorithms for the demand prediction of new product design based on sustainability.

Dr. Guangdong Tian
Prof. Dr. Kuan Yew Wong
Dr. Amir M. Fathollahi-Fard
Dr. Maxim A. Dulebenets
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.

Keywords

  • sustainable design
  • manufacturing systems
  • big data analytics
  • simulation and modeling
  • optimization
  • sustainable development

Published Papers (2 papers)

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22 pages, 6260 KiB  
Article
Integrated Optimization of Process Planning and Scheduling for Aerospace Complex Component Based on Honey-Bee Mating Algorithm
by Guozhe Yang, Qingze Tan, Zhiqiang Tian, Xingyu Jiang, Keqiang Chen, Yitao Lu, Weijun Liu and Peisheng Yuan
Appl. Sci. 2023, 13(8), 5190; https://doi.org/10.3390/app13085190 - 21 Apr 2023
Cited by 1 | Viewed by 1170
Abstract
To cope with the problems of poor matching between processing characteristics and manufacturing resources, low production efficiency, and the hard-to-meet dynamic and changeable model requirements in multi-variety and small batch aerospace enterprises, an integrated optimization method of complex component process planning and workshop [...] Read more.
To cope with the problems of poor matching between processing characteristics and manufacturing resources, low production efficiency, and the hard-to-meet dynamic and changeable model requirements in multi-variety and small batch aerospace enterprises, an integrated optimization method of complex component process planning and workshop scheduling for aerospace manufacturing enterprises is proposed. This paper considers the process flexibility of aerospace complex components comprehensively, and an integrated optimization model for the process planning and production scheduling of aerospace complex components is established with the optimization objectives of achieving a minimum makespan, machining time and machining cost. A honey-bee mating optimization algorithm (HBMO) combined with the greedy algorithm was proposed to solve the model. Then, it formulated a four-layer encoding method based on a feature-processing sequence, processing method, and machine tool, a tool was designed, and five worker bee cultivation strategies were designed to effectively solve the problems of infeasible solutions and local optimization when a queen bee mated to a drone. Finally, taking the complex component parts of an aerospace enterprise as an example, the integrated optimization of process planning and workshop scheduling is carried out. The results demonstrate that the proposed model and algorithm can effectively shorten the makespan and machining time, and reduce the machining cost. Full article
(This article belongs to the Special Issue Sustainable Manufacturing Systems Using Big Data Analytics)
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28 pages, 4703 KiB  
Article
Modelling Analysis of a Four-Way Shuttle-Based Storage and Retrieval System on the Basis of Operation Strategy
by Jia Mao, Jinyuan Cheng, Xiangyu Li, Honggang Zhao and Ciyun Lin
Appl. Sci. 2023, 13(5), 3306; https://doi.org/10.3390/app13053306 - 5 Mar 2023
Viewed by 1631
Abstract
In the context of sustainable development, this paper rationalises the outbound process of a four-way shuttle system with a focus on their modelling, performance evaluation and configuration using a parallel operation strategy to reduce resource waste, thus achieving sustainable development. The parallelism of [...] Read more.
In the context of sustainable development, this paper rationalises the outbound process of a four-way shuttle system with a focus on their modelling, performance evaluation and configuration using a parallel operation strategy to reduce resource waste, thus achieving sustainable development. The parallelism of the hoist and shuttle is innovatively incorporated into the four-way shuttle system, so the modelling content is divided into parallel and serial types. In the parallel operation strategy model, a separation–aggregation queueing network model is constructed, and the open-loop queueing network is innovatively solved using the maximum entropy method. In the serial operation strategy model, a semi-open-loop queuing network is constructed and solved using the geometric matrix method. By varying different parameters, the accuracy of the model is verified by Arena simulation with an error range of 10% or less, and the error of the system performance index calculation is reduced by 20% compared with the existing methods. Setting up 18 different sizes of shuttle systems provided a better performance than a single serial-operation strategy through the addition of parallel strategies, with an average reduction of 12.6% in the system response time and a minimum reduction of 1.8%. The conclusions of this paper were verified on the basis of an arithmetic case analysis. Full article
(This article belongs to the Special Issue Sustainable Manufacturing Systems Using Big Data Analytics)
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