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Systems
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Management and Simulation of Digitalized Smart Manufacturing Systems
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Management and Simulation of Digitalized Smart Manufacturing Systems

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Engineering".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 4537

Special Issue Editors

Dr. Mohammed M. Mabkhot

E-Mail Website
Guest Editor
Advanced VR Research Centre, Wolfson School of Mechanical, Electrical & Manufacturing Engineering, Loughborough University, Loughborough LE11 3AQ, UK
Interests: smart factories; discrete-event simulation; human-centric automation; knowledge management; model-based system engineering; manufacturing servitization; adaptive and reconfigurable systems; multi-criteria decision making; mathematical and bio-inspired optimization; sustainable manufacturing; I4.0 sustainable engineering education
Dr. Pedro Ferreira

E-Mail Website
Guest Editor
Intelligent Automation Centre, Wolfson School of Mechanical, Electrical, and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: symbiotic assembly systems; agent-based industrial control; cyber physical systems; human–machine interaction; collaborative robotics; self-adapting systems; self-learning systems; semantic technology; modular assembly systems; mechatronic systems; I4.0 engineering education
Dr. Dorota Stadnicka

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering and Aeronautics, Rzeszów University of Technology, 35-959 Rzeszów, Poland
Interests: production engineering; lean production; intelligent manufacturing systems; human–robot collaboration; sustainable development; human-centric manufacturing systems.
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart Manufacturing has emerged as a pivotal domain, bridging the gap between traditional production methodologies and the digital revolution. Rooted in the Industry 4.0 (I4.0) foundation and rapidly advancing towards human-centricity with a focus on manufacturing sustainability (I5.0), it is benefiting from the new revolution of Large Language Models (LLMs). This new era is characterized by the innovative integration of information, automation, computation, and networking in manufacturing processes. Key areas of interest include the enhancement of manufacturing intelligence through technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Cyber–Physical Production Systems (CPPS), making the sector more digitized and intelligent. These technologies drive the transformation towards more efficient, responsive, and adaptive manufacturing environments. With these technological advancements, manufacturing systems now have the capability to evolve from static, linear operations to dynamic, modular, and interconnected ecosystems that respond intelligently to varying production demands and challenges.

This Special Issue invites submissions that lay the groundwork for the next generation of digitized and human-centric smart manufacturing systems. The aim is to showcase research that contributes significantly to both the theoretical and practical aspects, bringing forth discussions that could influence both the academic sphere and the industrial sector.

We welcome submissions on a broad range of topics, including, but not limited to, the following:

  • Management frameworks and model innovations for smart manufacturing systems.
  • Simulation advancements in smart factories.
  • Advanced integration of IoT, AI, ML, and Reinforcement Learning within manufacturing, emphasizing sustainability and human-centricity.
  • Enhanced Circular Manufacturing: Leveraging Intelligent Reuse, Repurposing, and Remanufacturing Strategies.
  • Real-time monitoring and predictive maintenance via digital twin technologies.
  • Supply chain and logistics enhancement strategies within smart manufacturing.
  • The role of cyber–physical production systems in enhancing human–technology collaboration.
  • Human-centric design and operational strategies for smart manufacturing systems.
  • Manufacturing system servitization in software and hardware.
  • Novel approaches and methods from LLMs for improving human interaction.
  • Deep learning and lifelong learning for automating traditionally manual manufacturing tasks.
  • Adaptive and reconfigurable strategies for enhancing system resilience and flexibility in smart manufacturing environments.
  • New I5.0 competencies to equip the next generation for digital manufacturing.

Dr. Mohammed M. Mabkhot
Dr. Pedro Ferreira
Dr. Dorota Stadnicka
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. Systems is an international peer-reviewed open access monthly 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

  • smart manufacturing
  • Industry 5.0 (I5.0)
  • digital twin technologies
  • sustainable manufacturing
  • cyber-physical production systems (CPPS)
  • intelligent circular economy
  • human-centric automation
  • manufacturing servitization
  • collaborative robotics
  • supply chain optimization
  • adaptive manufacturing systems
  • reconfigurable manufacturing
  • digitalized real-time monitoring
  • predictive maintenance
  • large language models (llms)
  • artificial intelligence (ai)
  • machine learning (ml)
  • deep learning
  • lifelong learning

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Published Papers (3 papers)

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Research

24 pages, 4308 KiB  
Article
Evolutionary Game-Based New Energy Vehicle Supply Chain Strategies That Consider Carbon Reduction and Consumers’ Low-Carbon Preferences
by Yuanda Xu, Lian Han, Xing Li, Wenxing Zhu and Haiping Ren
Systems 2024, 12(9), 328; https://doi.org/10.3390/systems12090328 - 27 Aug 2024
Viewed by 922
Abstract
The rapid development of the new energy industry has intensified the competition among companies. Finding solutions to achieve technological innovation, carbon reduction, and to earn consumers’ confidence has become a pressing challenge. In this research, we aim to develop a four-party evolutionary game [...] Read more.
The rapid development of the new energy industry has intensified the competition among companies. Finding solutions to achieve technological innovation, carbon reduction, and to earn consumers’ confidence has become a pressing challenge. In this research, we aim to develop a four-party evolutionary game model involving government, manufacturers, dealers, and consumers to examine the strategic decisions made by these parties in order to accomplish carbon emission reduction goals. We will perform numerical simulations to analyze the strategic choices of each party and the relevant influencing factors. The results suggest the following: (1) The tax hike on traditional car production is less than the innovation expenses for new energy vehicles, leading manufacturers to lean towards manufacturing traditional vehicles. (2) The rise in taxes resulting from the manufacture of conventional vehicles will influence manufacturers’ strategic decisions, whereas the expenses related to technological advancements will have a more significant effect on manufacturers’ strategic choices. (3) Compared to dealers, manufacturers’ strategic choices are more significantly influenced by consumers’ awareness of low-carbon preferences. (4) In the early stages of technological innovation, the government typically offers incentive subsidies to manufacturers to boost technological innovation activities. Whereas, in the later stages of technological innovation, the government usually provides direct subsidies to consumers to encourage the market acceptance and widespread use of innovative products. Full article
(This article belongs to the Special Issue Management and Simulation of Digitalized Smart Manufacturing Systems)
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18 pages, 2925 KiB  
Article
Variable Neighborhood Search for Minimizing the Makespan in a Uniform Parallel Machine Scheduling
by Khaled Bamatraf and Anis Gharbi
Systems 2024, 12(6), 221; https://doi.org/10.3390/systems12060221 - 20 Jun 2024
Viewed by 1239
Abstract
This paper investigates a uniform parallel machine scheduling problem for makespan minimization. Due to the problem’s NP-hardness, much effort from researchers has been directed toward proposing heuristic and metaheuristic algorithms that can find an optimal or a near-optimal solution in a reasonable amount [...] Read more.
This paper investigates a uniform parallel machine scheduling problem for makespan minimization. Due to the problem’s NP-hardness, much effort from researchers has been directed toward proposing heuristic and metaheuristic algorithms that can find an optimal or a near-optimal solution in a reasonable amount of time. This work proposes two versions of a variable neighborhood search (VNS) algorithm with five neighborhood structures, differing in their initial solution generation strategy. The first uses the longest processing time (LPT) rule, while the second introduces a novel element by utilizing a randomized longest processing time (RLPT) rule. The neighborhood structures for both versions were modified from the literature to account for the variable processing times in uniform parallel machines. We evaluated the performance of both VNS versions using a numerical example, comparing them against a genetic algorithm and a tabu search from existing literature. Results showed that the proposed VNS algorithms were competitive and obtained the optimal solution with much less effort. Additionally, we assessed the performance of the VNS algorithms on randomly generated instances. For small-sized instances, we compared their performance against the optimal solution obtained from a mathematical formulation, and against lower bounds derived from the literature for larger instances. Computational results showed that the VNS version with the randomized LPT rule (RLPT) as the initial solution (RVNS) outperformed that with the LPT rule as the initial solution (LVNS). Moreover, RVNS found the optimal solution in 90.19% of the small instances and yielded an average relative gap of about 0.15% for all cases. Full article
(This article belongs to the Special Issue Management and Simulation of Digitalized Smart Manufacturing Systems)
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24 pages, 6054 KiB  
Article
Empowering Unskilled Production Systems Consultants through On-the-Job Training Support: A Digital Triplet Approach
by Takaomi Sato, Shinsuke Kondoh and Yasushi Umeda
Systems 2024, 12(5), 179; https://doi.org/10.3390/systems12050179 - 17 May 2024
Viewed by 1530
Abstract
This study aims to experimentally confirm whether knowledge that has been challenging to transfer through traditional on-the-job training (OJT) can be effectively transferred by introducing a formalized OJT approach that describes the improvement process knowledge of skilled production systems consultants, facilitating imitation by [...] Read more.
This study aims to experimentally confirm whether knowledge that has been challenging to transfer through traditional on-the-job training (OJT) can be effectively transferred by introducing a formalized OJT approach that describes the improvement process knowledge of skilled production systems consultants, facilitating imitation by unskilled consultants. We adopted the Digital Triplet (D3) concept, an extension of the authors’ digital twin framework to intelligent activities, aligning with our study objectives. Recognizing the difficulty and inadequacy of knowledge transfer in production systems consulting OJT, we propose an OJT support method integrating a decision-making modeling approach for skilled consultants’ processes based on the Generalized Production Systems Consulting Process Model (GCPM) from prior literature into traditional OJT methods involving self-learning and direct instruction. This method enables the construction of a domain-specific GCPM, formalizing the improvement process flow implemented by skilled consultants and linking it to production improvement expertise and tools. In a case study focused on energy-saving improvement, we constructed and tested a domain-specific GCPM’s efficacy in facilitating the transfer of difficult-to-transfer knowledge. The results indicate that domain-specific GCPM facilitates such knowledge transfer, including specialized improvement, knowledge utilization, rationale, and adaptation to specific cases. Full article
(This article belongs to the Special Issue Management and Simulation of Digitalized Smart Manufacturing Systems)
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