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Simulation, Modeling, and Decision-Making Processes in Manufacturing Systems and Industrial...
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Simulation, Modeling, and Decision-Making Processes in Manufacturing Systems and Industrial Engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 4044

Special Issue Editors

Prof. Dr. Chia-Nan Wang

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Interests: operations management; decision-making; management of technology; electronic commerce; systematic innovation
Special Issues, Collections and Topics in MDPI journals
Dr. Nhat Luong Nhieu

E-Mail Website
Guest Editor Assistant
Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Interests: multiple criteria decision-making; simulation; linear programming; optimization algorithms; AI-driven decision-making
Dr. Phan Van-Thanh

E-Mail Website
Guest Editor Assistant
Vietnam-Korea University of Information and Communication Technology, Danang 550000, Vietnam
Interests: grey system theory; time series forecasting; decision making; data envelopment analysis; measurement; operation management
Dr. Hector Tibo

E-Mail Website
Guest Editor Assistant
Department of Electrical Engineering and Technology, Technological University of the Philippines Taguig, Taguig City 1630, Philippines
Interests: data analysis; multicriteria decision making; forecasting; information technology; operation management

Special Issue Information

Dear Colleagues,

Within the dynamic and ever-evolving landscape of manufacturing systems and industrial engineering, an unparalleled synergy has emerged—one that wields the power to redefine the boundaries of innovation and optimization. At the heart of this transformative confluence lies the seamless integration of simulation, modeling, and decision-making processes, collectively constituting a driving force that propels the industrial world toward unprecedented efficiency, resilience, and sustainability.

In this era of relentless technological advancement, characterized by the rapid emergence of Industry 4.0, smart factories, and the relentless pursuit of excellence in industrial processes, the marriage of simulation, modeling, and decision-making has taken center stage. These intertwined disciplines not only illuminate the path toward innovation, but also serve as indispensable tools for understanding, optimizing, and reimagining the complex fabric of manufacturing systems and industrial engineering.

The Special Issue, entitled "Simulation, Modeling, and Decision-Making Processes in Manufacturing Systems and Industrial Engineering", serves as a dynamic platform for researchers, scholars, and practitioners to explore the cutting-edge developments at this critical intersection.

This Special Issue delves into the heart of contemporary manufacturing and industrial engineering, where simulation techniques, advanced modeling approaches, and astute decision-making strategies converge to address the multifaceted challenges of our times. It provides a comprehensive platform for contributions that illuminate emerging trends, novel methodologies, and innovative applications in this dynamic and ever-evolving field.

Prof. Dr. Chia-Nan Wang
Guest Editor

Dr. Nhat Luong Nhieu
Dr. Phan Van-Thanh
Dr. Hector Tibo
Guest Editor Assistants

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. Processes 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

  • Industry 4.0
  • modeling
  • innovation in decision-making
  • machine learning
  • artificial intelligence
  • multi-objective optimization
  • simulation
  • data-driven decision insights
  • industrial engineering
  • soft computing
  • adaptive decision support
  • manufacturing systems
  • sustainability
  • cybersecurity
  • process optimization
  • smart factories
  • industrial automation
  • production planning
  • operations research
  • other processes

Published Papers (6 papers)

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Research

16 pages, 654 KiB  
Article
Determinants for Supplier Selection Based on Hybrid Grey Theory: Case Study of the Vietnamese Coffee Industry
by Nguyen-Nhu-Y Ho, Phuong Mai Nguyen, Cong Thanh Tran and Huy Hung Ta
Processes 2024, 12(5), 901; https://doi.org/10.3390/pr12050901 (registering DOI) - 28 Apr 2024
Viewed by 99
Abstract
Coffee is not merely a refreshing beverage but also invigorates people, provides relaxation, contributes to human health, and fosters closer social connections. Coffee is one of the most widely consumed beverages worldwide and the most traded commercial commodity. Moreover, the rapid development of [...] Read more.
Coffee is not merely a refreshing beverage but also invigorates people, provides relaxation, contributes to human health, and fosters closer social connections. Coffee is one of the most widely consumed beverages worldwide and the most traded commercial commodity. Moreover, the rapid development of the Vietnamese coffee industry caused some concerns due to its insufficient performance and the fierce competition within the industry. It is significant to establish an efficient supply network; notwithstanding, supplier selection has always been a challenge for companies. Therefore, this paper employs a hybrid model to determine the supplier selection criteria, a vital factor for a manufacturer under practical operating conditions. Firstly, a combined model of Grey forecasting and the Grey Fourier series is applied to forecast future rainfall and temperature data for six consecutive years. Secondly, based on the criteria, strategies, and buyer requirements, the single-objective linear programming model helps identify the outperformed suppliers. The results found that prices and location change are determinants of supplier selection, and supplier shortage is an enormous barrier for the industry. In this study, these price forecasts allow supply chain management to make informed decisions about inventory levels, transportation routes, and resource allocation to ensure smooth operation and optimize coffee supply chain management. Full article
(This article belongs to the Special Issue Simulation, Modeling, and Decision-Making Processes in Manufacturing Systems and Industrial Engineering)
16 pages, 1554 KiB  
Article
Northern Lights: Prospecting Efficiency in Europe’s Renewable Energy Sector
by Yen-Hsing Hung and Fu-Chiang Yang
Processes 2024, 12(3), 618; https://doi.org/10.3390/pr12030618 - 20 Mar 2024
Viewed by 634
Abstract
Northern European nations are at the forefront of renewable energy adoption but face challenges in optimizing energy conversion efficiency. There is a lack of detailed understanding of how behavioral factors affect the efficiency of renewable energy conversion in these countries. This study aims [...] Read more.
Northern European nations are at the forefront of renewable energy adoption but face challenges in optimizing energy conversion efficiency. There is a lack of detailed understanding of how behavioral factors affect the efficiency of renewable energy conversion in these countries. This study aims to evaluate and compare the renewable energy conversion efficiency of Northern European countries, intending to inform strategic policy making and identify best practices for technology deployment in the renewable energy sector. Employing a Data Envelopment Analysis (DEA) model, the study integrates behavioral economic parameters—specifically, the aversion loss and gain significance coefficients—to assess the efficiency of renewable energy conversion, accounting for psychological factors in decision making. A comprehensive sensitivity analysis was conducted, varying the gain significance coefficient while maintaining the aversion loss coefficient at constant levels. This experiment was designed to observe the impact of behavioral parameters on the efficiency ranking of each country. The analysis revealed that Latvia consistently ranked highest in efficiency, irrespective of the gain significance valuation, whereas Iceland consistently ranked lowest. Other countries demonstrated varying efficiency rankings with changes in gain significance, indicating different behavioral economic influences on their renewable energy sectors. Theoretically, the study enhances the DEA framework by integrating behavioral economics, offering a more holistic view of efficiency in renewable energy. Practically, it provides a benchmarking perspective that can guide policy and investment in renewable energy, with sensitivity analysis underscoring the importance of considering behavioral factors. The research offers a practical tool for policymakers and energy stakeholders to align renewable energy strategies with behavioral incentives, aiming to improve the adoption and effectiveness of these initiatives. Full article
(This article belongs to the Special Issue Simulation, Modeling, and Decision-Making Processes in Manufacturing Systems and Industrial Engineering)
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13 pages, 3126 KiB  
Article
Finite Element Simulation of a Multistage Square Cup Drawing Process for Relatively Thin Sheet Metal through a Conical Die
by Walid M. Shewakh and Ibrahim M. Hassab-Allah
Processes 2024, 12(3), 525; https://doi.org/10.3390/pr12030525 - 06 Mar 2024
Viewed by 554
Abstract
A new manufacturing process has been developed that involves drawing circular sheets of thin metal through a conical die to create square cups. This technique produces deep square cups with a height-to-punch-side length ratio of approximately 2, as well as high dimensional accuracy [...] Read more.
A new manufacturing process has been developed that involves drawing circular sheets of thin metal through a conical die to create square cups. This technique produces deep square cups with a height-to-punch-side length ratio of approximately 2, as well as high dimensional accuracy and a nearly uniform height. The study investigated how various factors, including the sheet material properties and process geometric parameters, affect the limiting drawing ratio (LDR). The researchers used finite element analysis to determine the optimal die design for achieving a high LDR and found that the proposed technique is advantageous for producing long square cups with high dimensional accuracy. Full article
(This article belongs to the Special Issue Simulation, Modeling, and Decision-Making Processes in Manufacturing Systems and Industrial Engineering)
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Graphical abstract

17 pages, 1955 KiB  
Article
Method of Analyzing Technological Data in Metric Space in the Context of Industry 4.0
by Karolina Czerwińska and Andrzej Pacana
Processes 2024, 12(2), 401; https://doi.org/10.3390/pr12020401 - 17 Feb 2024
Viewed by 520
Abstract
The purpose of this article was to develop a method of analyzing the manufacturing process with variables indicating product competitiveness and technological capabilities in metric space as a cognitive source. The presented method will facilitate the identification of key development factors within the [...] Read more.
The purpose of this article was to develop a method of analyzing the manufacturing process with variables indicating product competitiveness and technological capabilities in metric space as a cognitive source. The presented method will facilitate the identification of key development factors within the manufacturing processes that have the greatest impact on the adaptation of the manufacturing enterprise to Industry 4.0. The presented method of manufacturing process analysis integrates a number of tools (SMART method, brainstorming, BOST analysis, 3 × 3 metrics) that enable the implementation of statistical analysis. The model developed makes it possible to apply known mathematical methods in areas new to them (adaptation in the manufacturing area), which makes it possible to use scientific information in a new way. The versatility of the method allows it to be used in manufacturing companies to identify critical factors in manufacturing processes. A test of the developed method was carried out in one of the foundry enterprises, which allowed us to build a series of importance factors affecting effective production management. The methodology is addressed to the management of manufacturing enterprises as a method to assist in analyzing data and building (on the basis of improved manufacturing processes) a competitive strategy. Full article
(This article belongs to the Special Issue Simulation, Modeling, and Decision-Making Processes in Manufacturing Systems and Industrial Engineering)
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18 pages, 4046 KiB  
Article
Optimizing Production Schedules: Balancing Worker Cooperation and Learning Dynamics in Seru Systems
by Weiguo Liu, Weizhe Dai and Xuyin Wang
Processes 2024, 12(1), 38; https://doi.org/10.3390/pr12010038 - 22 Dec 2023
Viewed by 652
Abstract
This paper aims to investigate the seru scheduling problem while considering the dual effects of worker cooperation and learning behavior to minimize the makespan and order processing time. Given the complexity of this research problem, an improved shuffled frog leaping algorithm based on [...] Read more.
This paper aims to investigate the seru scheduling problem while considering the dual effects of worker cooperation and learning behavior to minimize the makespan and order processing time. Given the complexity of this research problem, an improved shuffled frog leaping algorithm based on a genetic algorithm is proposed. We design a double-layer encoding based on the problem, introduce a single point and uniform crossover operator, and select the crossover method in probability form to complete the evolution of the meme group. To avoid damaging grouping information, the individual encoding structure is transformed into unit form. Finally, numerical experiments were conducted using numerical examples of large and small sizes for verification. The experimental results demonstrate the feasibility of the proposed model and algorithm, as well as the necessity of considering worker dual behavior in the seru scheduling problem. Full article
(This article belongs to the Special Issue Simulation, Modeling, and Decision-Making Processes in Manufacturing Systems and Industrial Engineering)
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16 pages, 6227 KiB  
Article
Structural Design and Analysis of a 100 kW Radial Turbine for an Ocean Thermal Energy Conversion–Organic Rankine Cycle Power Plant
by Xin Feng, Haoyang Li, Jie Huang, Qingfen Ma, Mao Lin, Jingru Li and Zhongye Wu
Processes 2023, 11(12), 3341; https://doi.org/10.3390/pr11123341 - 30 Nov 2023
Viewed by 643
Abstract
In this paper, a 100 kW radial inflow turbine is designed for an ocean thermal energy conversion (OTEC) power plant based on the organic Rankine cycle (ORC) with ammonia as the working fluid. Based on one-dimensional (1D) and three-dimensional computational fluid dynamics (3D-CFD) [...] Read more.
In this paper, a 100 kW radial inflow turbine is designed for an ocean thermal energy conversion (OTEC) power plant based on the organic Rankine cycle (ORC) with ammonia as the working fluid. Based on one-dimensional (1D) and three-dimensional computational fluid dynamics (3D-CFD) modeling, the mechanical structure design, static and modal analyses of the turbine and its components are carried out to investigate its mechanical performance. The results show the stress and strain distribution in the volute, stator and rotor, and their maximum values appear, respectively, at the inlet cutout, the tip of the stator outlet and the connection position between the rotor and the shaft. After optimization, all the stresses in the above components are below the allowable values. The frequencies from the first order to the sixth order of the rotor and whole turbine were obtained through modal analysis without prestress and under prestress. The maximum frequency of the rotor and whole turbine is 707.75 Hz and 40.22 Hz, both of which are far away from the resonance frequency range that can avoid resonance. Therefore, the structure of the designed turbine is safe, feasible and reliable so as to better guide actual production. Full article
(This article belongs to the Special Issue Simulation, Modeling, and Decision-Making Processes in Manufacturing Systems and Industrial Engineering)
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