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Design and Optimization of Manufacturing Systems, 2nd Edition

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

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 18339

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Prof. Dr. Zoran Jurković

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Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Interests: modeling and optimization of processes; machine tools; application of evolutionary algorithms and other natural-based algorithms; process efficiency; energy savings in production processes
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Dr. David Ištoković

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Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Interests: production planning and scheduling; simulation of production processes; batch sizing; operations management; optimization algorithms
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Dr. Janez Gotlih

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Faculty of Mechanical Engineering, University of Maribor, Smetanova ul. 17, 2000 Maribor, Slovenia
Interests: smart production and manufacturing engineering; Industry 4.0; robotics and assembly systems; collaborative systems; simulation modelling; machine learning and optimization in production engineering
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Special Issue Information

Dear Colleagues,

Today's market requires great efforts to ensure the survival and competitiveness of production companies, which are required to produce certain products of sufficient quality both rapidly and with low costs. In this context, activities related to the design of suitable manufacturing systems and the planning and optimization of the manufacturing process are of great importance. Production planning problems often have high computational complexity, and finding a suitable solution is extremely challenging. Traditional approaches to manufacturing system design and production planning are not capable of capturing information in real time and responding quickly to changes in the production environment. For this reason, new theoretical and practical solutions are of great importance.

This Special Issue of Applied Sciences, "Design and Optimization of Manufacturing Systems, 2nd Edition", seeks to collect research on the design and optimization of manufacturing systems to increase efficiency, reduce costs, and improve sustainability and other performance measures relevant to modern production environments. Papers that provide solutions to current real-world problems are also welcome. We look forward to receiving your contributions.

Prof. Dr. Zoran Jurković
Dr. David Ištoković
Dr. Janez Gotlih
Guest Editors

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Keywords

manufacturing systems
layout design
Industry 4.0
smart factory
production planning
scheduling
optimization
simulation

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Design and Optimization of Manufacturing Systems in Applied Sciences (11 articles)

Published Papers (10 papers)

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Research

17 pages, 6852 KiB

Open AccessArticle

Research on Quality Prediction for Thermal Printing Using a Particle Swarm Optimization with Back Propagation (PSO-BP) Neural Network

by Chun-Ling Ho, Zhiyun Wu, Tung-Chiung Chang and Shenjun Qi

Appl. Sci. 2025, 15(9), 5116; https://doi.org/10.3390/app15095116 - 4 May 2025

Viewed by 275

Abstract

Thermal printing is a prevalent method due to its advantages of rapid output, cost effectiveness, and ease of use. However, the quality of thermal printing is influenced by the printing speed, the temperature, and the concentration and characteristics of the materials. This research employs a BP neural network to forecast print quality, utilizing two activation functions. The findings indicate that a dual-layer hidden configuration utilizing the GeLU activation function yields a lower root-mean-square error (RMSE). The optimal configuration identified consists of six neurons in the first hidden layer and three neurons in the second hidden layer. To enhance the predictive performance, a PSO algorithm was integrated with the PSO-BP model to refine the parameter selection, which included ambient temperature, printing speed, and printing concentration, with iterative training and validation conducted via the gradient descent algorithm. The PSO-BP network achieved an MAE of 0.1108, an RMSE of 0.145, an MSE of 0.021, and an R² value of 0.9916 in predicting print quality. These results substantiate the stability and reliability of the neural network model developed with the PSO algorithm. Further validation with ten sets of test samples demonstrated that the model attained an average absolute error of 2.77% in print quality predictions, indicating robust generalization capabilities and precise forecasting. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems, 2nd Edition)

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22 pages, 3994 KiB

Open AccessArticle

Module Partition of Mechatronic Products Based on Core Part Hierarchical Clustering and Non-Core Part Association Analysis

by Shuai Wang, Yi-Fei Song, Guang-Yu Zou and Jia-Xiang Man

Appl. Sci. 2025, 15(5), 2322; https://doi.org/10.3390/app15052322 - 21 Feb 2025

Viewed by 438

Abstract

Production using modular architecture can not only shorten the product development cycle and improve the efficiency of product development, but also facilitate the upgrading of a product’s main functions and the recycling of materials. However, mechatronic products are plagued by various problems, such as greater difficulty in development and longer product development cycles due to their large numbers of parts with intricate internal relationships. However, the existing modular design method still faces problems when dealing with the modular design of mechatronic products. The structure of mechanical and electrical products is very complex, which is not conducive to the establishment of a model, and complex structural models lead to low efficiency and poor accuracy of module identification. Therefore, we propose an integrated module division method for mechatronic products based on core part hierarchical clustering and non-core part association analysis. Firstly, the core part screening method is used to simplify the structural model of mechatronic products and reduce the difficulty of modeling. Then, based on the core parts, the corresponding product design structural matrix (DSM) model is established. Secondly, the hierarchical clustering algorithm is used to obtain the module division scheme of different levels of mechatronic products, and the optimal modular scheme is obtained through an evaluation of modularity and a rationality analysis of module structure. Finally, based on the analysis of the association strength between the non-core parts and the existing modules, the non-core parts are classified into the corresponding product modules, and the final modularization scheme is obtained. A case study demonstrates the feasibility of the proposed method through the modular design of an electric bicycle. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems, 2nd Edition)

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14 pages, 6162 KiB

Open AccessArticle

Modal Analysis and Optimization of Tractor Exhaust System

by Ayla Tekin and Halil Şamlı

Appl. Sci. 2025, 15(4), 2070; https://doi.org/10.3390/app15042070 - 16 Feb 2025

Viewed by 487

Abstract

Excessive vibrations in exhaust systems can significantly reduce a vehicle’s lifespan and compromise performance. These vibrations, caused by factors such as engine operation and road conditions, lead to wear and tear. To address this issue, a finite element analysis (FEA) was conducted on a 90-horsepower tractor’s exhaust system. Using ANSYS WB^®, a 3D model was created and modal analysis was performed to determine the system’s natural frequencies and mode shapes. Based on the results, geometric modifications were made to the exhaust system, increasing its stiffness and shifting vibration frequencies to higher values. Consequently, vibration levels, noise, and the risk of component failure were significantly reduced. The redesigned exhaust system was successfully implemented in production. This study demonstrates the effectiveness of FEA in analyzing exhaust system vibrations and facilitating design improvements. By extending vehicle lifespan and providing a quieter, more comfortable driving experience, this research offers valuable insights for automotive and mechanical engineers. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems, 2nd Edition)

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15 pages, 4195 KiB

Open AccessArticle

Robotic Cell Layout Optimization Using a Genetic Algorithm

by Raúl-Alberto Sánchez-Sosa and Ernesto Chavero-Navarrete

Appl. Sci. 2024, 14(19), 8605; https://doi.org/10.3390/app14198605 - 24 Sep 2024

Cited by 4 | Viewed by 1834

Abstract

The design of the work area of a robotic cell is currently an iterative process of trial and improvement, where, in the best cases, the user places the workstations and robotic manipulators in a 3D virtual environment to then semi-automatically verify variables such as the robot’s reach, cycle time, geometric interferences, and collisions. This article suggests using an evolutionary computation algorithm (genetic algorithm) as a tool to solve this optimization problem. Using information about the work areas and the robot’s reach, the algorithm generates an equipment configuration that minimizes the cell area without interference between the stations and, therefore, reduces the distances the robotic manipulator must travel. The objective is to obtain an optimized layout of the workstations and to validate this optimization by comparing the transfer times between stations with the actual times of an existing screwdriving cell. As a result, the transfer time was reduced by 9%. It is concluded that the algorithm can optimize the layout of a robotic cell, which can lead to significant improvements in efficiency, quality, and flexibility. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems, 2nd Edition)

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23 pages, 6483 KiB

Open AccessArticle

Development of a Simulation Model to Improve the Functioning of Production Processes Using the FlexSim Tool

by Wojciech Lewicki, Mariusz Niekurzak and Jacek Wróbel

Appl. Sci. 2024, 14(16), 6957; https://doi.org/10.3390/app14166957 - 8 Aug 2024

Cited by 3 | Viewed by 3701

Abstract

One of the goals of Industry 4.0 is to increase the transparency of the value chain through modern tools in production processes. This article aims to discuss the possibility of increasing the efficiency of a production system by modernizing it with the use of computer modelling tools. This article describes a method for the simulation modelling of a selected production system using the specialized FlexSim 2023 software in a 3D environment. The results and benefits of the practical application of the object-oriented modelling are presented, as well as the possibilities of collecting simulation data used to optimize production processes. The analyses were conducted at a selected production plant in a case study. The research assessed the effectiveness of the existing system and determined the impact of process changes in the event of the introduction of a new design solution. The simulation identified bottlenecks in the material flow. The basis for creating the simulation model was the analysis of the technological process. A simulation model for a real situation was created, and a simulation model was designed to identify and indicate a solution to eliminate the detection of the bottleneck. The problem area identified using visualization in the technological process slowed down the entire production process and contributed to time and economic losses. Thus, the authors confirmed the thesis that the simulation modelling of production systems using the FlexSim program can help eliminate bottlenecks and increase the efficiency of human resource use. At the same time, the use of this tool can lead to increased efficiency, reduced costs and improved sustainability and other performance indicators important for modern production environments as part of the promoted Industry 4.0 idea. A noticeable result of these changes was an increase in production from about 80–90 units. In addition, it was noticed that the condition of the machines preceding the stand changed. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems, 2nd Edition)

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19 pages, 851 KiB

Open AccessArticle

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

by Sebastiano Gaiardelli, Damiano Carra, Stefano Spellini and Franco Fummi

Appl. Sci. 2024, 14(7), 3026; https://doi.org/10.3390/app14073026 - 3 Apr 2024

Cited by 2 | Viewed by 1655

Abstract

Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smooth operations. Despite extensive research exploring the impact of job characteristics, such as fixed or variable task-processing times and job arrival rates, the role of the transport system has been relatively underexplored. This paper specifically addresses the utilization of a conveyor belt as the primary mode of transportation among a set of production machines. In this configuration, no input or output buffers exist at the machines, and the transport times are contingent on machine availability. In order to tackle this challenge, we introduce a randomized heuristic approach designed to swiftly identify a near-optimal joint schedule for job processing and transfer. Our solution has undergone testing on both state-of-the-art benchmarks and real-world instances, showcasing its ability to accurately predict the overall processing time of a production line. With respect to our previous work, we specifically consider the case of the arrival of a dynamic job, which requires a different design approach since there is a need to keep track of partially processed jobs, jobs that are waiting, and newly arrived jobs. We adopt a total rescheduling strategy and, in order to show its performance, we consider a clairvoyant scheduling approach, in which job arrivals are known in advance. We show that the total rescheduling strategy yields a scheduling solution that is close to optimal. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems, 2nd Edition)

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25 pages, 7890 KiB

Open AccessArticle

SysML4GDPSim: A SysML Profile for Modeling Geometric Deviation Propagation in Multistage Manufacturing Systems Simulation

by Sergio Benavent-Nácher, Pedro Rosado Castellano and Fernando Romero Subirón

Appl. Sci. 2024, 14(5), 1830; https://doi.org/10.3390/app14051830 - 23 Feb 2024

Cited by 1 | Viewed by 1031

Abstract

In recent years, paradigms like production quality or zero-defect manufacturing have emerged, highlighting the need to improve quality and reduce waste in manufacturing systems. Although quality can be analyzed from various points of view during different stages of a manufacturing system’s lifecycle, this research focuses on a multidomain simulation model definition oriented toward the analysis of productivity and geometric quality during early design stages. To avoid inconsistencies, the authors explored the definition of descriptive models using system modeling language (SysML) profiles that capture domain-specific semantics defining object constraint language (OCL) rules, facilitating the assurance of model completeness and consistency regarding this specific knowledge. This paper presents a SysML profile for the simulation of geometric deviation propagation in multistage manufacturing systems (SysML4GDPSim), containing the concepts for the analysis of two data flows: (a) coupled discrete behavior simulation characteristic of manufacturing systems defined using discrete events simulation (DEVS) formalism; and (b) geometric deviation propagation through the system based on the geometrical modeling of artifacts using concepts from the topologically and technologically related surfaces (TTRS) theory. Consistency checking for this type of multidomain simulation model and the adoption of TTRS for the mathematical analysis of geometric deviations are the main contributions of this work, oriented towards facilitating the collaboration between design and analysis experts in the manufacturing domain. Finally, a case study shows the application of the proposed profile for the simulation model of an assembling line, including the model’s transformation to Modelica and some experimental results of this type of analysis. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems, 2nd Edition)

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16 pages, 2029 KiB

Open AccessArticle

Risk Management in Good Manufacturing Practice (GMP) Radiopharmaceutical Preparations

by Michela Poli, Mauro Quaglierini, Alessandro Zega, Silvia Pardini, Mauro Telleschi, Giorgio Iervasi and Letizia Guiducci

Appl. Sci. 2024, 14(4), 1584; https://doi.org/10.3390/app14041584 - 16 Feb 2024

Viewed by 3182

Abstract

Risk assessment and management during the entire production process of a radiopharmaceutical are pivotal factors in ensuring drug safety and quality. A methodology of quality risk assessment has been performed by integrating the advice reported in Eudralex, ICHQ, and ISO 9001, and its validity has been evaluated by applying it to real data collected in 21 months of activities of ¹⁸F-FDG production at Officina Farmaceutica, CNR-Pisa (Italy) to confirm whether the critical aspects that previously have been identified in the quality risk assessment were effective. The analysis of the results of the real data matched the hypotheses obtained from the model, and in particular, the most critical aspects were those related to human resources and staff organization with regard to management risk. Regarding the production process, the model of operational risk had predicted, as later confirmed by real data, that the most critical phase could be the synthesis and dispensing of the radiopharmaceuticals. So, the proposed method could be used by other similar radiopharmaceutical production sites to identify the critical phases of the production process and to act to improve performance and prevent failure in the entire cycle of radiopharmaceutical products. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems, 2nd Edition)

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19 pages, 3495 KiB

Open AccessArticle

Multi-AGV Scheduling under Limited Buffer Capacity and Battery Charging Using Simulation Techniques

by Jin-Sung Park and Jun-Woo Kim

Appl. Sci. 2024, 14(3), 1197; https://doi.org/10.3390/app14031197 - 31 Jan 2024

Cited by 5 | Viewed by 2302

Abstract

In recent years, automated guided vehicles (AGVs) have been widely adopted to automate material handling procedures in manufacturing shopfloors and distribution centers. AGV scheduling is the procedure of allocating a transportation task to an AGV, which has large impacts on the efficiency of an AGV system with multiple AGVs. In order to optimize the operations of multi-AGV systems, AGV scheduling decisions should be made with consideration of practical issues such as buffer space limitations and battery charging. However, previous studies have often overlooked those issues. To fill this gap, this paper proposes a simulation-based multi-AGV scheduling procedure for practical shopfloors with limited buffer capacity and battery charging. Furthermore, we propose three kinds of rules: job selection rules, AGV selection rules, and charging station selection rules, for AGV scheduling in practical shopfloors. The performance of the rules is evaluated through multi-scenario simulation experiments. The FlexSim software v.2022 is used to develop a simulation model for the experiments, and the experimental findings indicate that the job selection rules have larger impacts on the average waiting time than the other kinds of rules. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems, 2nd Edition)

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14 pages, 757 KiB

Open AccessArticle

Machine Learning Applied to Logistics Decision Making: Improvements to the Soybean Seed Classification Process

by Djonathan Luiz de Oliveira Quadras, Ian Cavalcante, Mirko Kück, Lúcio Galvão Mendes and Enzo Morosini Frazzon

Appl. Sci. 2023, 13(19), 10904; https://doi.org/10.3390/app131910904 - 30 Sep 2023

Cited by 2 | Viewed by 1780

Abstract

Soybean seed classification is a relevant and time-consuming process for Brazilian agribusiness cooperatives. This activity can generate queues and waiting times that directly affect logistics costs. This is the reason why it is so important to properly allocate resources, considering the most relevant factors that can influence their performance. This paper aims to present an approach to predicting the average lead time and waiting queue time for the soybean seed classification process, which supports the decision regarding the number of workers and machines to be deployed in the process. The originality of the paper relies on the applied approach, which combines discrete event simulation with machine learning algorithms in a real-world applied case. The approach comprises three steps: data collection to structure the simulation scenarios; simulation runs to generate artificial historical data; and machine learning applications to predict lead and queuing times. As a result, various scenarios using the data generated by machine learning were simulated, making it possible to choose the one that generated the best trade-off between performance, investments, and operational costs. The approach can be adapted to support the solution of different logistic-related decision-making problems that combine human and equipment resources. Full article

(This article belongs to the Special Issue Design and Optimization of Manufacturing Systems, 2nd Edition)

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