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Applied Sciences
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Design and Optimization of Manufacturing Systems, 2nd Edition
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Design and Optimization of Manufacturing Systems, 2nd Edition

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

Deadline for manuscript submissions: 20 September 2024 | Viewed by 3895

Special Issue Editors

Prof. Dr. Zoran Jurković

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Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. David Ištoković

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Janez Gotlih

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

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

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

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

Related Special Issue

Published Papers (5 papers)

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Research

19 pages, 851 KiB  
Article
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 - 03 Apr 2024
Viewed by 560
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 [...] Read more.
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  
Article
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
Viewed by 377
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 [...] Read more.
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  
Article
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 706
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 [...] Read more.
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  
Article
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
Viewed by 587
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 [...] Read more.
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  
Article
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
Viewed by 914
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 [...] Read more.
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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