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Sustainable Manufacturing Systems in the Context of Industry 4.0

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

Deadline for manuscript submissions: 1 June 2026 | Viewed by 7932

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Special Issue Editors

Dr. Jian Chen

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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

Dr. Yaowen Sang

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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

Dr. Jun Xu

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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

Dr. Penghao Cui

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Guest Editor

School of Business Administration, Northeastern University, Shenyang 110819, China
Interests: sustainable manufacturing; smart manufacturing systems; production control

Prof. Dr. Donald Huisingh

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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 250 words) can be sent to the Editorial Office for assessment.

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 (8 papers)

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Research

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23 pages, 809 KB

Open AccessArticle

Corporate Sustainability Systems Development Framework for Comfort Socks, Hosiery and Bodywear Textiles Production: Türkiye Case Study

by Saliha Karadayi-Usta

Sustainability 2026, 18(7), 3326; https://doi.org/10.3390/su18073326 - 30 Mar 2026

Viewed by 421

Abstract

The socks, hosiery, bodywear (SHB) industry is a critical segment of the textile sector, characterized by high-volume production and rapid delivery requirements, making efficiency and resource optimization essential. A corporate sustainability system is needed to minimize environmental impact, ensure long-term competitiveness, and align operations with global sustainability standards. Thus, this research aims to propose an integrated Corporate Sustainability System (CSS) framework that synergizes Lean Manufacturing (LM), Digital Transformation (DT), and sustainability transition through a methodological triangulation of (1) a narrative review, (2) in-depth expert interviews, and (3) a comprehensive Turkish case study. The proposed framework integrates foundational lean principles such as 5S, TPM, and Value Stream Mapping with Industry 4.0 technologies, including RFID traceability, real-time ERP integration and machine vision systems. Empirical demonstration through the case study reveals that establishing foundational lean maturity is a critical foundation for successful digital adoption. Furthermore, the study demonstrates that transitioning from manual tracking to integrated digital platforms resolves data silos and enhances the transparency of customer revisions and warehouse accuracy. The framework also incorporates human-centric Lean 5.0 improvements, proving that ergonomic interventions such as rail-mounted cable systems are vital for operational sustainability. Ultimately, the CSS provides a scalable model that aligns SHB production with global mandates like the EU Green Deal and CBAM, positioning the sector for long-term competitive advantage in an increasingly eco-conscious global market. Full article

(This article belongs to the Special Issue Sustainable Manufacturing Systems in the Context of Industry 4.0)

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31 pages, 5541 KB

Open AccessArticle

Preference-Guided Reinforcement Learning for Dynamic Green Flexible Assembly Job Shop Scheduling with Learning–Forgetting Effects

by Ruyi Wang, Xiaojuan Liao, Guangzhu Chen, Yaxin Liu and Leyuan Liu

Sustainability 2026, 18(7), 3222; https://doi.org/10.3390/su18073222 - 25 Mar 2026

Viewed by 624

Abstract

With the evolution from Industry 4.0 to 5.0, flexible assembly scheduling must simultaneously address production efficiency, environmental sustainability, and human factors, while remaining adaptive to real-time disruptions. This study investigates the dynamic green scheduling problem in dual-resource Flexible Assembly Job Shops with worker learning and forgetting, aiming to minimize makespan and total energy consumption. To tackle this problem, a Hierarchical Dual-Agent Deep Reinforcement Learning algorithm (HAD-DRL) is proposed. The framework integrates a Heterogeneous Graph Neural Network to extract real-time workshop states and employs two collaborative agents, i.e., a high-level preference decision agent and a low-level scheduling execution agent. The upper agent dynamically adjusts the preference weights between economic and environmental objectives, while the lower agent generates corresponding scheduling actions. Unlike existing multi-agent methods that optimize a single objective at each step, HAD-DRL achieves adaptive coordination and balanced trade-offs among conflicting goals. Experimental results demonstrate that the proposed method outperforms heuristic and baseline DRL approaches in both objectives, validating its effectiveness and practical applicability for intelligent and sustainable manufacturing. Full article

(This article belongs to the Special Issue Sustainable Manufacturing Systems in the Context of Industry 4.0)

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18 pages, 1420 KB

Open AccessArticle

Development of a Compass Framework to Achieve an Agile and Sustainable Supply Network

by Lucila Palandella, Lourdes Perea Muñoz and Angel Ruiz

Sustainability 2026, 18(4), 1865; https://doi.org/10.3390/su18041865 - 11 Feb 2026

Viewed by 515

Abstract

Digital transformation offers significant potential to reshape supply chains; however, implementation efforts remain fragmented, technology-centric, and insufficiently aligned with strategic, organizational, and sustainability goals. Existing frameworks and maturity models tend to emphasize the technological dimension, offering limited guidance on how digital transformation should be integrated with people, processes, culture, and sustainability at the supply network level. Building on evidence synthesized through an umbrella review of the state of the art, this paper proposes the Agile and Sustainable Supply Network Compass, a holistic and actionable framework designed to support organizations in advancing toward agile and sustainable supply networks. The Compass incorporates three structural dimensions—Strategy, Processes, and Capabilities (related to digitalization and sustainability)—as foundational pillars for transformation. We hypothesize that an effective transformation requires the joint alignment of strategy, cross-functional processes, and capabilities, as well as the explicit identification of a reduced supply network, a focal firm, and its critical linkages. The results show that positioning agility and sustainability as shared strategic objectives at the supply network level enables coherent decision-making, targeted capability development and improved coordination across interconnected actors. Rather than prescribing specific technologies, the proposed framework provides a guiding methodological logic that explains how digitalization and sustainability can co-evolve within supply networks. This work contributes to both theory and practice by bridging conceptual gaps in the literature and establishing the groundwork for future maturity models and empirical applications. Full article

(This article belongs to the Special Issue Sustainable Manufacturing Systems in the Context of Industry 4.0)

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22 pages, 3645 KB

Open AccessArticle

Artificial Intelligence Agents for Sustainable Production Based on Digital Model-Predictive Control

by Natalia Bakhtadze, Victor Dozortsev, Artem Vlasov, Mariya Koroleva and Maxim Anikin

Sustainability 2026, 18(2), 759; https://doi.org/10.3390/su18020759 - 12 Jan 2026

Viewed by 746

Abstract

The article presents an approach to synthesizing artificial intelligence agents (AI agents), in particular, control and decision support systems for process operators in various industries. Such a system contains an identifier in the feedback loop that generates digital predictive associative search models of the Just-in-Time Learning (JITL) type. It is demonstrated that the system can simultaneously solve (outside the control loop) two additional tasks: online operator pre-training and mutual adaptation of the operator and the system based on real-world production data. Solving the latter task is crucial for teaching the operator and the system collaborative handling of abnormal situations. AI agents improve control efficiency through self-learning, personalized operator support, and intelligent interface. Stabilization of process variables and minimization of deviations from optimal conditions make it possible to operate process plants close to constraints with sustainable product qualities. Along with higher yield of target product(s), this reduces equipment wear and tear, utilities consumption and associated harmful emissions. This is the key merit of Model Predictive Control (MPC) systems, which justify their application. JITL-type models proposed in the article are more precise than conventional ones used in MPC; therefore, they enable the operation even closer to process constraints. Altogether, this further improves the reliability of production systems and contributes to their sustainable development. Full article

(This article belongs to the Special Issue Sustainable Manufacturing Systems in the Context of Industry 4.0)

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27 pages, 1602 KB

Open AccessArticle

The Manufacturers’ Adoption of Green Manufacturing Under the Government’s Green Subsidy

by Wu Chen, Fei Ye and Yao Qiu

Sustainability 2025, 17(20), 9028; https://doi.org/10.3390/su17209028 - 12 Oct 2025

Viewed by 947

Abstract

As environmental degradation intensifies, governments increasingly subsidize green manufacturing to promote sustainability. This study develops a game-theoretic model of two competing supply chains, comprising original equipment manufacturers (OEMs) and both traditional and green contract manufacturers (CMs), to investigate the impacts of subsidies on green manufacturing adoption. Specifically, we construct a four-stage dynamic game model to examine the interactions among OEMs, CMs, and the government. The main findings are as follows: First, the government subsidy affects OEMs’ adoption decisions only if the production cost of green manufacturing or competition intensity is sufficiently high or if the market sensitivity to green products is relatively low. Second, the optimal subsidy level depends jointly on the production cost of green manufacturing, competition intensity, and market greenness sensitivity: when the production cost of green manufacturing is low (high), the subsidy should rise (fall) with market greenness sensitivity but fall (rise) with competition intensity. Third, while intensified competition reduces OEMs’ profits and overall supply chain performance, its impact on CMs and consumers depends on the production cost of green manufacturing; in contrast, greater consumer sensitivity to green products yields an all–win outcome for all stakeholders. These results yield important managerial implications. For policymakers, when the production costs of green manufacturing are relatively low, green subsidies should be scaled back as market competition intensifies. For manufacturers, it is critical to carefully evaluate the production costs of green manufacturing and the level of government subsidies and to strategically pursue first-mover advantages in advancing sustainable operations, thereby fostering an all-win outcome for stakeholders. Full article

(This article belongs to the Special Issue Sustainable Manufacturing Systems in the Context of Industry 4.0)

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19 pages, 2289 KB

Open AccessArticle

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 1059

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 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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Review

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34 pages, 2897 KB

Open AccessReview

Remanufacturing Scheduling Toward Sustainable Economy: A Comprehensive Analysis on Academic Research and Industry Practice

by Wengang Zheng, Zhun Li, Yubin Wang, Xinwang Liu, Ke Cao, Zhengang Yuan, Wenjie Wang, Gang Yuan, Zhiqiang Tian and Honghao Zhang

Sustainability 2026, 18(8), 3662; https://doi.org/10.3390/su18083662 - 8 Apr 2026

Viewed by 370

Abstract

As an important part of green manufacturing, remanufacturing has important practical significance for alleviating resource shortage and waste, developing circular economy and promoting sustainable development. In recent years, remanufacturing scheduling (RS), which can achieve high efficiency and green remanufacturing through the reasonable allocation of resources, has become a research hotspot in the field of remanufacturing. To offer a comprehensive evaluation of the research dynamics and development trends of RS, this paper systematically reviews the publications from 2010 to 2025 via Scopus, Web of Science, and the IEEE Xplore database. Firstly, the research background of RS, related remanufacturing policies and the generalized connotation of remanufacturing are introduced. Then, selected and valid publications are analyzed from time aspect, country aspect, and keyword aspect through Citespace software. In addition, based on remanufacturing level, modeling idea, optimization objectives, solution method, production scenarios and practical application, publications are further grouped and reviewed. In addition, according to the research gap existing in recent studies, some future development trends are accordingly pointed out, aiming to provide valuable insights for research related to RS. Finally, meaningful conclusions are drawn and the importance of RS is emphasized once again. Full article

(This article belongs to the Special Issue Sustainable Manufacturing Systems in the Context of Industry 4.0)

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36 pages, 7810 KB

Open AccessReview

A Comprehensive Review of Human-Robot Collaborative Manufacturing Systems: Technologies, Applications, and Future Trends

by Qixiang Cai, Jinmin Han, Xiao Zhou, Shuaijie Zhao, Lunyou Li, Huangmin Liu, Chenhao Xu, Jingtao Chen, Changchun Liu and Haihua Zhu

Sustainability 2026, 18(1), 515; https://doi.org/10.3390/su18010515 - 4 Jan 2026

Cited by 1 | Viewed by 2403

Abstract

Amid the dual-driven trends of Industry 5.0 and smart manufacturing integration, as well as the global imperative for manufacturing sustainability to address resource constraints, carbon neutrality goals, and circular economy demands, human–robot collaborative (HRC) manufacturing has emerged as a core direction for reshaping manufacturing production modes while aligning with sustainable development principles. This paper comprehensively reviews HRC manufacturing systems, summarizing their technical framework, practical applications, and development trends with a focus on the synergistic realization of operational efficiency and sustainability. Addressing the rigidity of traditional automated lines, inefficiency of manual production, and the unsustainable drawbacks of high energy consumption and resource waste in conventional manufacturing, HRC integrates humans’ flexible decision-making and environmental adaptability with robots’ high-precision and continuous operation, not only improving production efficiency, quality, and safety but also optimizing resource allocation, reducing energy consumption, and minimizing production waste to bolster manufacturing sustainability. Its core technologies include task allocation, multimodal perception, augmented interaction (AR/VR/MR), digital twin-driven integration, adaptive motion control, and real-time decision-making, all of which can be tailored to support sustainable production scenarios such as energy-efficient process scheduling and circular material utilization. These technologies have been applied in automotive, aeronautical, astronautical, and shipping industries, boosting high-end equipment manufacturing innovation while advancing the sector’s sustainability performance. Finally, challenges and future directions of HRC are discussed, emphasizing its pivotal role in driving manufacturing toward a balanced development of efficiency, intelligence, flexibility, and sustainability. Full article

(This article belongs to the Special Issue Sustainable Manufacturing Systems in the Context of Industry 4.0)

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