applsci-logo

Journal Browser

Journal Browser

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems Volume II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 June 2025) | Viewed by 11705

Special Issue Editors


E-Mail Website
Guest Editor
Faculty of Manufacturing Technologies, Technical University of Košice, 040 01 Presov, Slovakia
Interests: industrial engineering; production planning; manufacturing management; optimization algorithms; production engineering; mass customization; optimization methods; logistics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Manufacturing Technologies, Technical University of Košice, 040 01 Presov, Slovakia
Interests: production planning and scheduling; manufacturing management; simulation; mass customization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous Special Issue, titled “Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems”.

Smart manufacturing is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy or equivalent national policies, and brings new challenges and opportunities for companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but as an important practice within organizations, since it significantly increases the productivity of manufacturing processes and brings other benefits for companies and their customers. The introduction of smart manufacturing systems is associated with the adaptation of the Internet of Things, cyber physical systems, artificial intelligence, advanced robotics, cloud technology, and so forth. Moreover, the implementation of these technologies is paving the way for the digital evolution, which is impacting almost all industries and sectors worldwide. However, recent studies have shown that pre-existing managerial methods and philosophies such as lean manufacturing, reconfigurable manufacturing systems or cellular manufacturing systems are of the utmost importance for the concept of smart manufacturing. In this context, manufacturing system design and scheduling methods should be further improved. As a prime example of co-existence, traditional, existing manufacturing methods and I4.0 technologies are efforts to develop integrative models supporting both lean manufacturing tools and I4.0 technologies. This Special Issue aims to collect original contributions related to designing and scheduling smart manufacturing systems.

Potential topics include, but are not limited to, the following:

  • Modern methods and techniques for designing layout manufacturing systems;
  • Innovative approaches for solving manufacturing cell formation problems;
  • Modelling and designing flexible and reconfigurable manufacturing systems;
  • Heuristics and metaheuristics for solving facility layout design problems;
  • Heuristics and metaheuristics for solving scheduling problems;
  • Multiobjective methods and techniques for solving design problems;
  • Modeling manufacturing processes for smart cyber physical environments;
  • Modeling assembly processes for mass customized manufacturing;
  • Design of architecture for human–robot collaborative assembly systems;
  • Multiobjective optimization of mixed-model assembly line balancing problems.

Prof. Dr. Vladimir Modrak
Dr. Zuzana Soltysova
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. Applied Sciences 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

  • optimization
  • facility layout design
  • assembly line balancing
  • mass customization
  • scheduling problem

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

5 pages, 156 KB  
Editorial
Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems Volume II
by Vladimir Modrak and Zuzana Soltysova
Appl. Sci. 2025, 15(16), 9066; https://doi.org/10.3390/app15169066 - 18 Aug 2025
Viewed by 521
Abstract
Following the success of the first volume of this Special Issue, entitled “Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems” [...] Full article

Research

Jump to: Editorial

19 pages, 2785 KB  
Article
Implementing an AI-Based Digital Twin Analysis System for Real-Time Decision Support in a Custom-Made Sportswear SME
by Tõnis Raamets, Kristo Karjust, Jüri Majak and Aigar Hermaste
Appl. Sci. 2025, 15(14), 7952; https://doi.org/10.3390/app15147952 - 17 Jul 2025
Cited by 1 | Viewed by 559
Abstract
Small and medium-sized enterprises (SMEs) in the manufacturing sector often struggle to make effective use of production data due to fragmented systems and limited digital infrastructure. This paper presents a case study of implementing an AI-enhanced digital twin in a custom sportswear manufacturing [...] Read more.
Small and medium-sized enterprises (SMEs) in the manufacturing sector often struggle to make effective use of production data due to fragmented systems and limited digital infrastructure. This paper presents a case study of implementing an AI-enhanced digital twin in a custom sportswear manufacturing SME developed under the AI and Robotics Estonia (AIRE) initiative. The solution integrates real-time production data collection using the Digital Manufacturing Support Application (DIMUSA); data processing and control; clustering-based data analysis; and virtual simulation for evaluating improvement scenarios. The framework was applied in a live production environment to analyze workstation-level performance, identify recurring bottlenecks, and provide interpretable visual insights for decision-makers. K-means clustering and DBSCAN were used to group operational states and detect process anomalies, while simulation was employed to model production flow and assess potential interventions. The results demonstrate how even a lightweight AI-driven system can support human-centered decision-making, improve process transparency, and serve as a scalable foundation for Industry 5.0-aligned digital transformation in SMEs. Full article
Show Figures

Figure 1

26 pages, 4805 KB  
Article
A Decision Framework for Selecting Highly Sustainable Packaging Circular Model in Mass-Customized Packaging Industry
by Ravishankar Rajendran and Sudhakarapandian Ranjitharamasamy
Appl. Sci. 2024, 14(22), 10224; https://doi.org/10.3390/app142210224 - 7 Nov 2024
Cited by 1 | Viewed by 2712
Abstract
The selection of a sustainable packaging circular model approach entails numerous obstacles under rapidly developing circumstances, such as environmental factors, market competition, and advancing technology, impacting decision-making processes. We have considered Z-number-based decision-making methods as an alternative to the conventional method. This study [...] Read more.
The selection of a sustainable packaging circular model approach entails numerous obstacles under rapidly developing circumstances, such as environmental factors, market competition, and advancing technology, impacting decision-making processes. We have considered Z-number-based decision-making methods as an alternative to the conventional method. This study presents a selection of circular sustainable packaging models, considering significant challenges from five primary objectives: economic, environmental, social responsibility, sustainability, and time-based, with three circular models: biodegradable, compostable, and recycling. The ZF-DEMATEL-TOPSIS method is used in an integrated manner to address the packaging circular model selection problem. The study results indicate that the mass-customized recyclable packaging circular model is the most highly sustainable among the three models. At the same time, the most significant challenges are production cost, energy efficiency, and makespan. The proposed method was validated using the sensitivity analysis with an 90% consistency ratio. We conducted this study to aid in analyzing and developing a highly sustainable mass-customized circular packaging model. Full article
Show Figures

Figure 1

18 pages, 3649 KB  
Article
Truck Transportation Scheduling for a New Transport Mode of Battery-Swapping Trucks in Open-Pit Mines
by Yufeng Xiao, Wei Zhou, Boyu Luan, Keyi Yang and Yuqing Yang
Appl. Sci. 2024, 14(22), 10185; https://doi.org/10.3390/app142210185 - 6 Nov 2024
Cited by 2 | Viewed by 1692
Abstract
To address the scheduling challenges associated with the increasing deployment of battery-swapping trucks in open-pit mines, this study proposes a multi-objective scheduling optimization model. This model accounts for the unique characteristics of battery-swapping trucks by incorporating constraints related to battery swapping alerts, the [...] Read more.
To address the scheduling challenges associated with the increasing deployment of battery-swapping trucks in open-pit mines, this study proposes a multi-objective scheduling optimization model. This model accounts for the unique characteristics of battery-swapping trucks by incorporating constraints related to battery swapping alerts, the selection of battery-swapping stations, and the impact of ambient temperature on battery capacity. The primary objective is to minimize the total haulage cost and total waiting time. Both a genetic algorithm and an adaptive genetic algorithm are applied to solve the proposed multi-objective scheduling optimization model. The aim is to identify an optimal scheduling solution without violating any model constraints. Results demonstrate that both the basic genetic algorithm and the adaptive genetic algorithm effectively achieve truck transportation scheduling. However, the adaptive genetic algorithm surpasses the basic genetic algorithm, reducing the total transportation costs by 5.6% and total waiting time by 17.4%. It also reduces the number of battery swaps and transportation distance by 15.8% and 1.2%, respectively. The proposed multi-objective scheduling optimization model successfully minimizes the waiting time and transportation costs of battery-swapping trucks while ensuring the completion of production tasks. This approach provides valuable technical support for improving the production and transportation efficiency of open-pit mining operations. Full article
Show Figures

Figure 1

19 pages, 2148 KB  
Article
Cell Formation and Intra-Cell Optimal Machine Location in CMS: A Novel Genetic Algorithm (GA) Based on Machine Encoding
by Xuanyi Wu, Wenling Li, Muhammad Rizwan, Qazi Salman Khalid, Mohammed Alkahtani and Fahad M. Alqahtani
Appl. Sci. 2023, 13(22), 12323; https://doi.org/10.3390/app132212323 - 14 Nov 2023
Cited by 2 | Viewed by 1652
Abstract
Manufacturing industries are in a constant state of competition to attract customers in a variety of methods. Group Technology (GT) is a term used in the field of manufacturing for grouping similar elements based on their similarities in production and design. Cellular manufacturing [...] Read more.
Manufacturing industries are in a constant state of competition to attract customers in a variety of methods. Group Technology (GT) is a term used in the field of manufacturing for grouping similar elements based on their similarities in production and design. Cellular manufacturing (CM) is an application of Group Technology (GT) that has gained widespread traction in Small- and Medium-Sized Enterprises (SMEs) during the recent years in order to increase the production floor’s efficiency and output. A Cell Formation consists of grouping identical machinery and assigning them on similar functions. There are three main decisions involved in designing the Cellular Manufacturing System (CMS): Group Scheduling (GS), Group Layout (GL), and Cell Formation (CF). In this study, the primary challenge associated with the CMS is not only the formation of cells but also the optimal placement of machinery within each cell. This paper’s objectives are therefore twofold: the formation of cells and the optimal placement of machinery within cells. For the purpose of Cell Formation and the position of machinery within the cell, a Genetic Algorithm (GA) and Encoding Scheme are employed. In this study, a Genetic Algorithm is used to classify machines and parts, while MATLAB is used for the simulation and encoding scheme. To evaluate the developed objective function and GA, a layout problem of medium size is solved. Results indicate that the proposed strategy is effective for resolving CMS issues and increasing productivity by 8.85%. Full article
Show Figures

Figure 1

22 pages, 21938 KB  
Article
Optimization Method of Assembly Tolerance Types Based on Degree of Freedom
by Guanghao Liu, Meifa Huang and Leilei Chen
Appl. Sci. 2023, 13(17), 9774; https://doi.org/10.3390/app13179774 - 29 Aug 2023
Cited by 2 | Viewed by 1754
Abstract
The automatic generation of tolerance specifications is an important aspect of achieving digital product design. An obvious feature of the current automatic generation of tolerance based on rule reasoning is that all tolerance types will be inferred for the same assembly feature. However, [...] Read more.
The automatic generation of tolerance specifications is an important aspect of achieving digital product design. An obvious feature of the current automatic generation of tolerance based on rule reasoning is that all tolerance types will be inferred for the same assembly feature. However, when labelling part tolerance information, designers need to further screen based on the geometric function of the assembly, which may result in prioritizing tolerance types that do not meet the geometric requirements of the assembly. This paper presents an assembly tolerance type optimization method based on the degree of freedom (DOF) of tolerance zone for the optimization and screening problem after reasoning all possible tolerance types. Firstly, we define the DOF of tolerance zones and their representations, while also define the control parameter degrees of freedom (CPDF) of assemblies, and analyze the CPDF of typical geometric functional tolerances of assemblies; Secondly, the Boolean operation relationship between sets is used to construct a Boolean operation preference method for the CPDF. Then, an algorithm for the optimal selection of the shape and position tolerance items of the assembly is established based on the DOFs of tolerance zone. Finally, the proposed method is verified by an engineering example, and the result shows that the method can optimize and screen the geometric tolerance types of assemblies. Full article
Show Figures

Figure 1

18 pages, 3699 KB  
Article
Influence of Manufacturing Process Modularity on Lead Time Performances and Complexity
by Vladimir Modrak and Zuzana Soltysova
Appl. Sci. 2023, 13(12), 7196; https://doi.org/10.3390/app13127196 - 16 Jun 2023
Cited by 5 | Viewed by 1689
Abstract
In principle, modular or integral character of manufacturing lines depends on the topological designs of products and determined operation tasks. On the other hand, in specific situations there is an articulated need for modular design in smart manufacturing systems since modular layouts are [...] Read more.
In principle, modular or integral character of manufacturing lines depends on the topological designs of products and determined operation tasks. On the other hand, in specific situations there is an articulated need for modular design in smart manufacturing systems since modular layouts are a crucial step towards agile production via smart manufacturing. The aim of this paper is to explore how the modular layout relates to manufacturing lead time (MLT) and to operational complexity of smart manufacturing systems. For this purpose, topologically different models of alternative process layouts were simulated and tested, while MLT values were obtained using Tecnomatix Plant Simulation. The obtained positive findings of this research could be useful not only in selection of the most suitable process design from the alternative ones, but especially in deepening the knowledge and bettering understanding of the concept of optimal network modularity. Full article
Show Figures

Figure 1

Back to TopTop