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Sustainable Manufacturing Systems in the Context of Industry 4.0

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Management".

Deadline for manuscript submissions: 1 December 2025 | Viewed by 501

Special Issue Editors


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Guest Editor
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: scheduling; operations management; smart manufacturing; optimization algorithms
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: scheduling theory; smart manufacturing; algorithm design and analysis; metaheuristics; combinatorial optimization

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Guest Editor
Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China
Interests: scheduling theory; smart manufacturing; digital twin
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Business Administration, Northeastern University, Shenyang 110819, China
Interests: sustainable manufacturing; smart manufacturing systems; production control
*
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Guest Editor
Institute for a Secure and Sustainable Environment, University of Tennessee, Knoxville, TN, USA
Interests: sustainable development; environmental management; environmental analysis; natural resource management; climate change adaptation
* Retired

Special Issue Information

Dear Colleagues,

Traditional manufacturing systems are often associated with high levels of resource consumption, waste generation, and environmental pollution. As industries face increasing pressure from governments, consumers, and environmental organizations to reduce their ecological footprints, the integration of sustainable practises becomes imperative. Digital technologies such as artificial intelligence (AI), the Internet of Things (IoT), and Big Data, in the context of Industry 4.0, offer new opportunities to address these challenges.

The resulting approaches will include integrating environmental, health, and safety concerns with green-product design, lean and green operations, real-time monitoring, energy-efficient scheduling, green logistics, and circular supply chains. These innovations provide the tools to drive sustainability in ways that were previously not possible and will help us transition to equitable, sustainable, post-fossil carbon societies based on renewable energy and improved energy efficiency at all levels.

The Special Issue is designed to provide insights into how Industry 4.0 can reshape the manufacturing sector in a way to contribute to long-term environmental, economic, and social sustainability. Specifically, it focuses on ways that emerging technologies of Industry 4.0, such as AI, IoT, and Big Data, can be leveraged to enhance sustainability goals, improve efficiency, reduce environmental footprints, and foster green supply chains for manufacturing systems.

This Special Issue will include research papers, case studies, and reviews in the field of sustainable manufacturing systems related to sustainability and to intelligent decision-making. 

Dr. Jian Chen
Dr. Yaowen Sang
Dr. Jun Xu
Dr. Penghao Cui
Prof. Donald Huisingh
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. 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

  • energy-efficient scheduling
  • smart factories based upon eco-design
  • closed-loop supply chains
  • green logistics and sustainable sourcing
  • digital twins and automation in green manufacturing
  • lean and green operations
  • data analytics for decision-making
  • remanufacturing and disassembly
  • human–robot collaboration manufacturing
  • “Human Intelligence” closely monitoring all of the AI processes

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Published Papers (1 paper)

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Research

19 pages, 2289 KiB  
Article
A Dynamic Energy-Saving Control Method for Multistage Manufacturing Systems with Product Quality Scrap
by Penghao Cui and Xiaoping Lu
Sustainability 2025, 17(13), 6164; https://doi.org/10.3390/su17136164 - 4 Jul 2025
Viewed by 235
Abstract
Manufacturing industries are increasingly focused on enhancing energy efficiency while maintaining high levels of production throughput and product quality. However, most existing energy-saving control (EC) methods overlook the influence of production quality on overall energy performance. To address this challenge, this paper proposes [...] Read more.
Manufacturing industries are increasingly focused on enhancing energy efficiency while maintaining high levels of production throughput and product quality. However, most existing energy-saving control (EC) methods overlook the influence of production quality on overall energy performance. To address this challenge, this paper proposes a dynamic EC method for multistage manufacturing systems with product quality scrap. The method utilizes a Markov decision process (MDP) framework to dynamically control the operational states of all machines based on real-time system conditions. Specifically, for two-stage manufacturing systems, the dynamic EC problem is formulated as an MDP, and the optimal EC policy is obtained by a dynamic programming algorithm. For multistage manufacturing systems, to address the curse of dimensionality, an aggregation procedure is proposed to approximate the optimal EC policy for each machine based on the results of two-stage manufacturing systems. Finally, numerical experiments are performed to demonstrate the effectiveness of the proposed dynamic EC method. For a five-stage manufacturing system, the proposed dynamic EC policy achieves a 13.55% reduction in energy consumption costs and a 3.02% improvement in system throughput compared to the baseline. Extensive case studies demonstrate that the dynamic EC policy consistently outperforms three well-studied methods: the station-level EC policy, the upstream-buffer EC policy, and the energy saving opportunity window policy. Moreover, the results confirm the effectiveness of the proposed method in capturing the influence of product quality scrap on the system energy efficiency. This study presents a sensor-integrated methodology for EC, contributing to the advancement of smart manufacturing practices in alignment with Industry 4.0 initiatives. Full article
(This article belongs to the Special Issue Sustainable Manufacturing Systems in the Context of Industry 4.0)
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