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Intelligent Manufacturing for Sustainability

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 1907

Special Issue Editor


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Guest Editor
Department of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Interests: intelligent manufacturing; artificial intelligence; data mining; knowledge management; supply chain

Special Issue Information

Dear Colleagues,

In the 21st century, with the continuous development of digital, networked, and intelligent technologies, the information technology and manufacturing industry has become deeply integrated. Intelligent manufacturing, as the product of this in-depth integration, has greatly improved the flexibility and productivity of traditional manufacturing and has become a hot topic globally. However, the rapid development of intelligent manufacturing has caused a series of societal problems, such as energy crisis, environmental pollution, and global warming, and financial risk transmission on supply chain. Manufacturers worldwide urgently need to consider the sustainability of manufacturing processes to balance the manufacturing efficiency, economic benefits, and social benefits, and financial risk considerations, and thus realize the sustainable development of the manufacturing industry and society.

This Special Issue aims to collect and publish a selection of high-quality research works related to intelligent manufacturing for sustainability. The topics of interest in this Special Issue include but are not limited to the following:

  • Development of modeling and simulation technologies to improve manufacturing efficiency and energy efficiency;
  • Development of an effective method to balance the manufacturing efficiency and environmental benefits;
  • Innovation of a manufacturing system configuration;
  • Development of integrated parts of sustainable manufacturing, including green manufacturing, remanufacturing, green supply chain, and reverse logistics;
  • Improvement of the remanufacturing efficiency of end-of-life products;
  • Sustainable management of product lifecycle and product multi-lifecycle.
  • Financial risk management including manufacturers’ financial risk control and customers credit scoring.

Prof. Wenyu Zhang
Guest Editor

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. Sustainability 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

  • sustainability
  • sustainable manufacturing
  • clean production
  • energy-aware manufacturing
  • intelligent manufacturing
  • environmental protection
  • remanufacturing
  • green manufacturing
  • green supply chain
  • product multilifecycle
  • sustainable supply chain
  • financial risk control

Published Papers (1 paper)

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Research

22 pages, 3921 KiB  
Article
A Mass-Customization-Based Remanufacturing Scheme Design Method for Used Products
by Wei Zhou and Chao Ke
Sustainability 2022, 14(16), 10059; https://doi.org/10.3390/su141610059 - 14 Aug 2022
Cited by 3 | Viewed by 1466
Abstract
Remanufacturing scheme design (RSD) is an essential step in the restoration and upgrading of used products. However, the quantity of remanufactured products is growing rapidly, and customers have personalized demands for remanufactured products that lead to shorter design cycles. In addition, the used [...] Read more.
Remanufacturing scheme design (RSD) is an essential step in the restoration and upgrading of used products. However, the quantity of remanufactured products is growing rapidly, and customers have personalized demands for remanufactured products that lead to shorter design cycles. In addition, the used products are scrapped due to their own defects, such as performance failure and functional degradation, which correspond to the inherent remanufacturing demand (IRD) of used products. Faced with large quantities of used products, how to quickly develop reasonable remanufacturing schemes for satisfying customers’ individual demands and the IRD is an urgent problem to be solved. To address these issues, a mass customization-based RSD method is proposed. First, remanufacturing demand comprising customer demand and the IRD is analyzed to determine the RSD targets and remanufacturing types. Then, the RSD methods are intelligently selected based on the remanufacturing types, which include restorative remanufacturing, upgrade remanufacturing and hybrid remanufacturing, while the hybrid contains restorative remanufacturing and upgrade remanufacturing. Moreover, the restorative remanufacturing scheme is generated to satisfy the restorative remanufacturing targets based on reverse engineering (RE) and the tool contact point path section line (TCPPSL) method. After used products are restored, case-based reasoning (CBR) is used to retrieve the case that best matches the upgrade remanufacturing targets, while the grey relational analysis (GRA) algorithm is applied to calculate the similarity between cases. Finally, the feasibility of this method is verified by considering the RSD of a used lathe. The results indicated that the proposed approach can rapidly help designers to obtain remanufacturing solutions for satisfying the customer demand and IRD. Full article
(This article belongs to the Special Issue Intelligent Manufacturing for Sustainability)
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