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Large-Scale Production Systems: Sustainable Manufacturing and Service
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Large-Scale Production Systems: Sustainable Manufacturing and Service

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

Deadline for manuscript submissions: closed (30 September 2025) | Viewed by 5644

Special Issue Editors

Dr. Simone Arena

E-Mail Website
Guest Editor
Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Via Marengo 2, 09123 Cagliari, Italy
Interests: asset management; maintenance; manufacturing systems; machine learning; fault diagnosis; health prognosis; condition monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandra Cantini

E-Mail Website
Guest Editor
Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via Lambruschini 4B, 20156 Milan, Italy
Interests: supply chain management; spare parts; inventory management; explainable artificial intelligence; additive manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Filippo De Carlo

E-Mail Website
Guest Editor
Department of Industrial Engineering (DIEF), University of Florence, 50134 Florence, Italy
Interests: industrial plant engineering; maintenance; industrial safety and risk; reliability; manufacturing systems; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays sustainability is a crucial issue influencing the development of new concepts, models, perspectives, and decision-support systems in manufacturing. This is an important goal in commercial strategy and operations, with applications from supply chain network design to production system management, including maintenance-related aspects. In particular, large-scale manufacturing facilities are responsible for consuming many environmental, energetic, and economic resources due to their wide spatial distribution, division of labor, extensive use of machinery, and multitude of components. Consequently, it is imperative to develop new strategies (or to demonstrate the adoption of existing ones) for enhancing the efficiency of manufacturing activities and services within large-scale production systems (LSPSs), mitigating the negative impact of LSPSs on global sustainability. LSPSs are industrial and engineering systems composed of numerous interconnected components (subsystems) that work in a coordinated way, often from remote locations, over extensive areas, all while operating under resource constraints. Examples of LSPSs include (but are not limited to) water distribution systems, large automotive manufacturing plants, large dairy production systems, textile industries, iron and steel industries, oil and gas systems, food processing systems, etc.

Modern LSPSs can benefit from the widespread use of digital technologies driving the Industry 4.0 paradigm, such as IoT-enabled tools, big data architectures, digital connectivity, blockchain, and cyber–physical systems, to achieve more sustainable production and consumption patterns. However, it is worth mentioning that sustainable development within LSPSs is a hard task due to several challenges. First, due to intricate interdependencies among components, LSPSs are prone to suffering from various faults, where even failure to detect a single component’s failure can damage the functioning of the entire LSPS (causing downtimes, financial losses, and safety hazards for both operators and environment). Therefore, to ensure the efficiency and sustainability of manufacturing and services within LSPSs, maintenance strategies must be optimized. Furthermore, beyond the need to ensure the proper functioning of individual components (based on pre-defined conditions), LSPSs require the adoption of strategic operational and control approaches. Particularly, depending on the available resources, operational and control strategies must be devised that allow for meeting specific demands effectively.

These challenges pose the need to investigate new progress and trends to increase the sustainability of LSPSs. Therefore, this Special Issue aims to present novel ideas and experimental results in the domain of the sustainable development of manufacturing and services within LSPSs, including both theoretical and practical investigations. Additionally, ideas conceived for improving the sustainability of lower-scale industrial plants, extensible to LSPSs, are appreciated. In this Special Issue, original research articles and reviews are welcome.

We look forward to receiving your contributions.

Dr. Simone Arena
Dr. Alessandra Cantini
Prof. Dr. Filippo De Carlo
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

  • large-scale production systems
  • big size plants
  • sustainable manufacturing
  • sustainable services
  • operations management
  • industrial plants

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Published Papers (3 papers)

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Research

22 pages, 1585 KB  
Article
Sustainable Control of Large-Scale Industrial Systems via Approximate Optimal Switching with Standard Regulators
by Alexander Chupin, Zhanna Chupina, Oksana Ovchinnikova, Marina Bolsunovskaya, Alexander Leksashov and Svetlana Shirokova
Sustainability 2025, 17(20), 9337; https://doi.org/10.3390/su17209337 - 21 Oct 2025
Viewed by 196
Abstract
Large-scale production systems (LSPS) operate under growing complexity driven by digital transformation, tighter environmental regulations, and the demand for resilient and resource-efficient operation. Conventional control strategies, particularly PID and isodromic regulators, remain dominant in industrial automation due to their simplicity and robustness; however, [...] Read more.
Large-scale production systems (LSPS) operate under growing complexity driven by digital transformation, tighter environmental regulations, and the demand for resilient and resource-efficient operation. Conventional control strategies, particularly PID and isodromic regulators, remain dominant in industrial automation due to their simplicity and robustness; however, their capability to achieve near-optimal performance is limited under constraints on control amplitude, rate, and energy consumption. This study develops an analytical–computational approach for the approximate realization of optimal nonlinear control using standard regulator architectures. The method determines switching moments analytically and incorporates practical feasibility conditions that account for nonlinearities, measurement noise, and actuator limitations. A comprehensive robustness analysis and simulation-based validation were conducted across four representative industrial scenarios—energy, chemical, logistics, and metallurgy. The results show that the proposed control strategy reduces transient duration by up to 20%, decreases overshoot by a factor of three, and lowers transient energy losses by 5–8% compared with baseline configurations, while maintaining bounded-input–bounded-output (BIBO) stability under parameter uncertainty and external disturbances. The framework provides a clear implementation pathway combining analytical tuning with observer-based derivative estimation, ensuring applicability in real industrial environments without requiring complex computational infrastructure. From a broader sustainability perspective, the proposed method contributes to the reliability, energy efficiency, and longevity of industrial systems. By reducing transient energy demand and mechanical wear, it supports sustainable production practices consistent with the following United Nations Sustainable Development Goals—SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production). The presented results confirm both the theoretical soundness and practical feasibility of the approach, while experimental validation on physical setups is identified as a promising direction for future research. Full article
(This article belongs to the Special Issue Large-Scale Production Systems: Sustainable Manufacturing and Service)
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22 pages, 1890 KB  
Article
Multi-Objective Optimization for Intermodal Freight Transportation Planning: A Sustainable Service Network Design Approach
by Alexander Chupin, Abdelaal Ahmed Mostafa Ahmed Ragas, Marina Bolsunovskaya, Alexander Leksashov and Svetlana Shirokova
Sustainability 2025, 17(12), 5541; https://doi.org/10.3390/su17125541 - 16 Jun 2025
Viewed by 2563
Abstract
Modern logistics requires effective solutions for the optimization of intermodal transportation, providing cost reduction and improvement of transport flows. This paper proposes a multi-objective optimization method for intermodal freight transportation planning within the framework of sustainable service network design. The approach aims to [...] Read more.
Modern logistics requires effective solutions for the optimization of intermodal transportation, providing cost reduction and improvement of transport flows. This paper proposes a multi-objective optimization method for intermodal freight transportation planning within the framework of sustainable service network design. The approach aims to balance economic efficiency and environmental sustainability by minimizing both transportation costs and delivery time. A bi-criteria mathematical model is developed and solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III), which is well-suited for handling complex, large-scale optimization problems under multiple constraints. The aim of the study is to develop and implement this technology that balances economic efficiency, environmental sustainability and manageability of operational processes. The research includes the development of a two-criteria model that takes into account both temporal and economic parameters of the routes. The optimization method employs the NSGA-III, a well-known metaheuristic that generates a diverse set of near-optimal Pareto-efficient solutions. This enables the selection of trade-off alternatives depending on the decision-maker’s preferences and specific operational constraints. Simulation results show that the implementation of the proposed technology can reduce the costs of intermodal operators by an average of 8% and the duration of transportation by up to 50% compared to traditional planning methods. In addition, the automation of the process contributes to a more rational use of resources, reducing carbon emissions and increasing the sustainability of transportation networks. This approach is in line with the principles of sustainable economic development, as it improves the efficiency of logistics operations, reduces pressure on infrastructure and minimizes the environmental impact of the transport sector. Route optimization and digitalization of transport processes can increase resource efficiency, improve freight flow management and contribute to the long-term stability of transport systems. The developed technology of automated planning of intermodal transportation is oriented to application in large-scale production systems, providing effective management of cargo flows within complex logistics chains. The proposed method supports the principles of sustainable development by providing a formal decision-making framework that balances transportation cost, delivery time and environmental objectives. Instead of optimizing for a single goal, the model enables the identification of efficient trade-offs between economic performance and ecological impact. Moreover, by generating multiple routing scenarios under varying operational constraints, the approach enhances the adaptability and robustness of freight transportation systems in dynamic and uncertain environments. Full article
(This article belongs to the Special Issue Large-Scale Production Systems: Sustainable Manufacturing and Service)
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24 pages, 2450 KB  
Article
From People to Performance: Leveraging Soft Lean Practices for Environmental Sustainability in Large-Scale Production
by Matteo Ferrazzi, Guilherme Luz Tortorella, Wen Li, Federica Costa and Alberto Portioli-Staudacher
Sustainability 2025, 17(9), 3955; https://doi.org/10.3390/su17093955 - 28 Apr 2025
Cited by 2 | Viewed by 1673
Abstract
Lean manufacturing can be considered a socio-technical system integrating both technical tools and human-centered, or soft, practices. While extensive research has examined technical aspects, the contribution of soft Lean practices focused on human behavior to environmental sustainability remains underexplored. This study addresses this [...] Read more.
Lean manufacturing can be considered a socio-technical system integrating both technical tools and human-centered, or soft, practices. While extensive research has examined technical aspects, the contribution of soft Lean practices focused on human behavior to environmental sustainability remains underexplored. This study addresses this gap by examining how soft Lean practices can help overcome barriers to environmental performance in large-scale production systems (LSPSs), using Italy’s food manufacturing sector as a case study. A multi-case study methodology was employed, involving five companies. Data were collected through interviews conducted across top management, middle management, and operational staff levels to capture diverse perspectives. Using variables extracted from the literature and a deductive coding approach, the study identifies (1) the specific soft Lean practices adopted and the perceived environmental performance barriers at each hierarchical level, (2) differences in interpretation of these practices and barriers across hierarchical levels, and (3) how soft practices can mitigate obstacles to sustainable performance. The results demonstrate that soft Lean practices, when aligned with organizational structure and culture, can effectively mitigate barriers to environmental improvement. This research contributes to the Lean and sustainability literature by offering a multi-level perspective and practical insights into integrating human-centered approaches within industrial sustainability strategies. Full article
(This article belongs to the Special Issue Large-Scale Production Systems: Sustainable Manufacturing and Service)
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