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Machines
Special Issues
Digital Twin-Driven Smart Production, Logistics, and Supply Chains
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Digital Twin-Driven Smart Production, Logistics, and Supply Chains

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 5132

Special Issue Editors

Prof. Dr. Sang Do Noh

E-Mail Website
Guest Editor
Department of Systems Management Engineering, Sungkyunkwan University, Suwon-si 16417, Republic of Korea
Interests: manufacturing systems; modeling and simulation; CAD/CAM/PLM/digital manufacturing; smart manufacturing; cyber physical system; digital twin
Special Issues, Collections and Topics in MDPI journals
Dr. Kyu-Tae Park

E-Mail Website
Guest Editor
Department of Industrial Engineering, Seoul National University of Science & Technology, Seoul 01811, Republic of Korea
Interests: asset description; cyber physical system; digital twin; distributed simulation; modeling and simulation

Special Issue Information

Dear Colleagues,

Recently, the three main goals for smart operations have been outlined as (a) human-centric, (b) resilience, and (c) sustainability. To achieve these, digital twins (DTs) continue to attract significant attention as a core operational technology. The DT has emerged as one of the most representative technologies for smart operations across various industrial sectors, which involve production, logistics, and supply chains. The DT can be defined as an advanced virtual asset that represents a configuration of physical assets, reflects complex and heterogeneous functional units, and synchronizes static and dynamic information objects derived from the physical assets. Through vertical and horizontal integrations, the core technical functionalities of the DT include monitoring, control, simulation, visualization, diagnosis, and predictive analysis. Moreover, the ideal implementation and application of DTs is essential for the construction of cyber-physical systems in diverse industrial sectors.

Numerous studies on DTs have been proposed to elaborate on human-centric, resilience, and sustainable operation perspectives. These studies contain efficient models, frameworks, methods, and supportive algorithms. Although existing studies have facilitated the implementation and application of DTs, there are massive opportunities and necessities to improve and extend such research. In the areas of production, logistics, and supply chains with human-centric, resilience, and sustainability perspectives, studies on DTs must be improved to accomplish self-configuration and fulfil autonomous operation to industrial assets. In addition, there are difficulties of federations between DTs in multiple systems that include several stakeholders and necessities of the efficient processing, management, and synchronization of an enormous amount of heterogenous information elements.

For these reasons, this Special Issue aims to present novel models, frameworks, methods, and supportive algorithms related to DTs as the core operational technology in areas of production, logistics, and supply chains.

We invite papers that address, but which are not limited to, the following topics:

  • Advanced and event-driven simulations using digital twins.
  • Asset descriptions for the synchronization of digital twins.
  • Conceptual models and architectural frameworks to support digital twins in production, logistics, and supply chains.
  • The coordination of new emerging technologies for the implementation of digital twins.
  • Data-driven modeling and simulation of digital twins.
  • Digital twins for human–robot collaborations, ergonomic analyses, and human-centric operations.
  • Digital twins for material handling devices (e.g., autonomous mobile robots, delivery drones, industrial robots, etc.)
  • Digital twins for resilient operation control and business processes.
  • Digital twins for sustainable operations in industrial systems (e.g., manufacturing systems, warehouses, distribution centers, etc.)
  • Industrial applications the digital twins as real-world case studies.
  • Information aggregation, fusion, and synchronization methods for digital twins.
  • Supportive rules and algorithms for the implementation and application of digital twins.

Prof. Dr. Sang Do Noh
Dr. Kyu-Tae Park
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. Machines 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

  • cyber-physical system (CPS)
  • digital twin
  • operational technology
  • smart production
  • smart logistics
  • smart supply chain

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Published Papers (2 papers)

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Research

23 pages, 1808 KiB  
Article
Cyber-Physical Scheduling System for Multiobjective Scheduling Optimization of a Suspension Chain Workshop Using the Improved Non-Dominated Sorting Genetic Algorithm II
by Wenbin Zhao, Junhan Hu, Jiansha Lu and Wenzhu Zhang
Machines 2024, 12(9), 666; https://doi.org/10.3390/machines12090666 - 23 Sep 2024
Viewed by 865
Abstract
Cyber-Physical Systems (CPSs) offer significant potential to address the evolving demands of industrial development. In the Industry 4.0 era, a framework integrating sensing, data exchange, numerical analysis, and real-time feedback is essential for meeting modern industrial needs. However, implementing this integration presents challenges [...] Read more.
Cyber-Physical Systems (CPSs) offer significant potential to address the evolving demands of industrial development. In the Industry 4.0 era, a framework integrating sensing, data exchange, numerical analysis, and real-time feedback is essential for meeting modern industrial needs. However, implementing this integration presents challenges across multiple domains, particularly in digital analysis, information sensing, and data exchange during corporate transformation. Companies yet to undergo transformation face distinct challenges, including the risks and trial-and-error costs of adopting new technologies. This study focuses on a heavy-duty workpiece processing factory, with a specific emphasis on the painting process. The complexity of this process frequently results in congestion, which is approached as a multi-objective, multi-constraint optimization problem. This paper proposes the Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) to address the requirements of multi-objective optimization. The proposed approach uses multi-chromosome structures, listeners, and recursive backtracking initialization to optimize the search for solutions under constraints. This enables the factory to automatically streamline production lines based on workpiece processing sequences, leading to increased efficiency. Additionally, a CPS framework focused on simulation modeling has been designed. First, the INSGA-II algorithm processes order data to generate production schedules. Next, the data transmission formats and input-output interfaces are designed. Then, a simulation model is built using the algorithm’s results. These components collectively form the CPS framework for this study. The proposed method offers an automated digital solution through the algorithm, enabling verification of its feasibility via the simulation model. As a result, it significantly enhances decision-making speed, reliability, and equipment utilization. Full article
(This article belongs to the Special Issue Digital Twin-Driven Smart Production, Logistics, and Supply Chains)
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22 pages, 5549 KiB  
Article
Optimization of the Factory Layout and Production Flow Using Production-Simulation-Based Reinforcement Learning
by Hyekyung Choi, Seokhwan Yu, DongHyun Lee, Sang Do Noh, Sanghoon Ji, Horim Kim, Hyunsik Yoon, Minsu Kwon and Jagyu Han
Machines 2024, 12(6), 390; https://doi.org/10.3390/machines12060390 - 5 Jun 2024
Cited by 1 | Viewed by 3561
Abstract
Poor layout designs in manufacturing facilities severely reduce production efficiency and increase short- and long-term costs. Analyzing and deriving efficient layouts for novel line designs or improvements to existing lines considering both the layout design and logistics flow is crucial. In this study, [...] Read more.
Poor layout designs in manufacturing facilities severely reduce production efficiency and increase short- and long-term costs. Analyzing and deriving efficient layouts for novel line designs or improvements to existing lines considering both the layout design and logistics flow is crucial. In this study, we performed production simulation in the design phase for factory layout optimization and used reinforcement learning to derive the optimal factory layout. To facilitate factory-wide layout design, we considered the facility layout, logistics movement paths, and the use of automated guided vehicles (AGVs). The reinforcement-learning process for optimizing each component of the layout was implemented in a multilayer manner, and the optimization results were applied to the design production simulation for verification. Moreover, a flexible simulation system was developed. Users can efficiently review and execute alternative scenarios by considering both facility and logistics layouts in the workspace. By emphasizing the redesign and reuse of the simulation model, we achieved layout optimization through an automated process and propose a flexible simulation system that can adapt to various environments through a multilayered modular approach. By adjusting weights and considering various conditions, throughput increased by 0.3%, logistics movement distance was reduced by 3.8%, and the number of AGVs required was reduced by 11%. Full article
(This article belongs to the Special Issue Digital Twin-Driven Smart Production, Logistics, and Supply Chains)
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