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Advances in Smart Industrial Engineering Techniques for Optimizing and Controlling...
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Advances in Smart Industrial Engineering Techniques for Optimizing and Controlling Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 18278

Special Issue Editor

Prof. Dr. Chien-Chih Wang

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 24301, Taiwan
Interests: machine learning and AI applications; process quality control and engineering optimization; machine vision and inspection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industrial Engineering and Management (also known as Industrial Engineering or IE)  is a highly systematic and widely applicable discipline combining science, engineering, information technology, and management for the study and optimization of organization performance. To develop practical solutions for improving operational efficiency, industrial engineers need to comprehensively and systematically understand, identify, and evaluate the complex interactions between an organization's departments, units, and subsystems. Industrial engineering has been critical for productivity enhancement, total quality management, and mass production since the second industrial revolution. With the arrival and development of the Industry 4.0 era, global manufacturing is undergoing significant changes. Industrial engineering has evolved with emerging concepts and technologies, bringing new opportunities and challenges for the industrial revolution in the Industry 4.0 era.

This Special Issue focuses on using industrial engineering techniques to solve challenges associated with optimizing and controlling enterprise processes using intelligent industrial engineering techniques. Researchers are encouraged to submit manuscripts on the broad, multidisciplinary topic of IE. Areas of interest include, but are not limited to, the following:

  • Predictive maintenance, quality control, lean six sigma, and process optimization;
  • Smart manufacturing process monitoring and control;
  • Intelligent manufacturing diagnostics, prognostics, energy management, and decision support methods;
  • Operations research, scheduling, system simulation, and supply chain management;
  • Robotics and human-machine interaction;
  • Industry 3.5, Industry 4.0, and Industry 5.0.

Prof. Dr. Chien-Chih Wang
Guest Editor

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

  • quality control
  • process optimization
  • smart manufacturing process monitoring and control
  • intelligent manufacturing diagnostics
  • energy management
  • decision support methods
  • system simulation
  • supply chain management
  • robotics and human-machine interaction

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

Published Papers (7 papers)

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Research

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20 pages, 7101 KiB  
Article
Probabilistic Fuzzy System for Evaluation and Classification in Failure Mode and Effect Analysis
by José Jovani Cardiel-Ortega and Roberto Baeza-Serrato
Processes 2024, 12(6), 1197; https://doi.org/10.3390/pr12061197 - 11 Jun 2024
Cited by 2 | Viewed by 1743
Abstract
Failure Mode and Effect Analysis (FMEA) is an essential risk analysis tool that is widely applicable in various industrial sectors. This structured technique allows us to identify and assign priority levels to potential failures that violate the reliability of a system or process. [...] Read more.
Failure Mode and Effect Analysis (FMEA) is an essential risk analysis tool that is widely applicable in various industrial sectors. This structured technique allows us to identify and assign priority levels to potential failures that violate the reliability of a system or process. Failure evaluation occurs in a decision-making environment with uncertainty. This study proposes a probabilistic fuzzy system that integrates linguistic and stochastic uncertainty based on a Mamdani-type model to strengthen the FMEA technique. The system is based on analyzing the frequency of failures and obtaining the parameters to determine the probability of occurrence through the Poisson distribution. In addition, the severity and detection criteria were evaluated by the experts and modeled using the Binomial distribution. The evaluation result is a discrete value analogous to the process of obtaining the success or failure of the expert generating the evaluation of 10 Bernoulli experiments. Three fuzzy inference expert systems were developed to combine multiple experts’ opinions and reduce linguistic subjectivity. The case study was implemented in the knitting area of a textile company in the south of Guanajuato to validate the proposed approach. The potential failure of the knitting machinery, which compromises the top tension subsystem’s performance and the product’s quality, was analyzed. The proposed system, which is based on a robust mathematical model, allows for reliable fault evaluation with a simple scale. The classification performed by the system and the one performed by the experts has similar behavior. The results show that the proposed approach supports decision-making by prioritizing failure modes. Full article
(This article belongs to the Special Issue Advances in Smart Industrial Engineering Techniques for Optimizing and Controlling Processes)
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21 pages, 2361 KiB  
Article
Procedure for Aggregating Indicators of Quality and Life-Cycle Assessment (LCA) in the Product-Improvement Process
by Andrzej Pacana and Dominika Siwiec
Processes 2024, 12(4), 811; https://doi.org/10.3390/pr12040811 - 17 Apr 2024
Cited by 3 | Viewed by 1551
Abstract
Sustainable product development requires combining aspects, including quality and environmental. This is a difficult task to accomplish. Therefore, procedures are being sought to combine these aspects in the process of product improvement. Therefore, the objective of the investigation was to develop a procedure [...] Read more.
Sustainable product development requires combining aspects, including quality and environmental. This is a difficult task to accomplish. Therefore, procedures are being sought to combine these aspects in the process of product improvement. Therefore, the objective of the investigation was to develop a procedure that supports the integration of quality-level indicators and life-cycle assessment (LCA) to determine the direction of product improvement. The procedure involves determining the quality indicators based on the expectations of the customer, which are subsequently processed using the formalised scoring method (PS). A life-cycle assessment index is determined for the main environmental impact criterion. According to the proposed mathematical model, these indicators are aggregated, and this process takes into account their importance in terms of product usefulness and environmental friendliness. Interpretations of the results and the direction of product improvement are from the results obtained from the modified IPA model (importance–performance analysis). The procedure is used in the verification of product prototypes, wherein the proposed approach, and its test, was carried out for a self-cooling beverage can (and its alternatives) with a “chill-on-demand” system, which is a technology supporting rapid cooling on demand. The life-cycle assessment was carried out to assess the carbon footprint, which is crucial for activities to reduce greenhouse gases. The direction of improvement of this product was shown to concern the selection of transport means, the reduction of energy use in the production phase, or the change of the method of opening the can. What is original is the proposal of a procedure for integrating the quality indicator and the life-cycle assessment indicator, taking into account the key environmental burden. The procedure can be used in manufacturing companies when designing and improving products in terms of their sustainable development. Full article
(This article belongs to the Special Issue Advances in Smart Industrial Engineering Techniques for Optimizing and Controlling Processes)
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13 pages, 1364 KiB  
Article
An Amended Crow Search Algorithm for Hybrid Active Power Filter Design
by Shoyab Ali, Annapurna Bhargava, Akash Saxena, Abdulaziz S. Almazyad, Karam M. Sallam and Ali Wagdy Mohamed
Processes 2023, 11(9), 2550; https://doi.org/10.3390/pr11092550 - 25 Aug 2023
Cited by 2 | Viewed by 1520
Abstract
Hybrid Active Power Filter (HAPF) imbibes the advantages of both passive and active power filters. These filters are considered one of the important technologies for mitigating harmonic pollution in electrical systems. Accurate estimation of filter parameters is a key component to reduce harmonic [...] Read more.
Hybrid Active Power Filter (HAPF) imbibes the advantages of both passive and active power filters. These filters are considered one of the important technologies for mitigating harmonic pollution in electrical systems. Accurate estimation of filter parameters is a key component to reduce harmonic pollution effectively. In recent years, several optimization approaches have been reported to solve this estimation problem; still, this area is worthy of further investigation. This paper is a proposal for an estimator that can estimate the parameter of HAPF configuration accurately. For evolving this estimator, first, an objective function that mathematically embeds filter parameters and harmonic pollution is presented. For handling the optimization process, an Amended Crow Search Algorithm (ACSA) is proposed. ACSA employs a local search algorithm (in the form of a pattern search) for obtaining optimal results. The analysis of the estimation process is carried out on two HAPF configurations. Various analyses that include harmonic pollution statistical analysis along with fitness function value analysis reveal that the proposed algorithm acquires optimal results as compared with other recently published and reported algorithms. Further, the proposed filter configurations are tested with the existing filter. The results prove that the proposed filter shows promising results. Full article
(This article belongs to the Special Issue Advances in Smart Industrial Engineering Techniques for Optimizing and Controlling Processes)
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19 pages, 1283 KiB  
Article
Vehicle Routing Problem Model with Practicality
by SeJoon Park, Chunghun Ha and Hyesung Seok
Processes 2023, 11(3), 654; https://doi.org/10.3390/pr11030654 - 21 Feb 2023
Viewed by 2088
Abstract
Truck platooning has recently become an essential issue in automatic driving. Though truck platooning can increase safety and reduce fuel consumption and carbon emissions, the practical vehicle routing problem involved in truck platooning has not been sufficiently addressed. Therefore, we design a mixed-integer [...] Read more.
Truck platooning has recently become an essential issue in automatic driving. Though truck platooning can increase safety and reduce fuel consumption and carbon emissions, the practical vehicle routing problem involved in truck platooning has not been sufficiently addressed. Therefore, we design a mixed-integer linear programming model for the routing problem in truck platooning considering the deadline of vehicles, continuous-time units, different fuel reduction rates, traffic congestion avoidance, and heterogeneous vehicles. In addition, a forward–backward heuristic called the “greedy heuristic” is presented for reasonable computation time. To validate the model’s performance, several parameters, such as the percentage of fuel reduction, percentage of detour vehicles, and percentage of platooned links (road segments), are considered. Additionally, various cases are considered with varying fuel reduction rates, traffic flow rates, and time windows. Full article
(This article belongs to the Special Issue Advances in Smart Industrial Engineering Techniques for Optimizing and Controlling Processes)
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19 pages, 3788 KiB  
Article
A Novel CSAHP Approach to Assess the Priority of Maintenance Work Outsourced by a Metro Company
by Sung-Neng Peng, Chien-Yi Huang and Hwa-Dong Liu
Processes 2023, 11(1), 100; https://doi.org/10.3390/pr11010100 - 29 Dec 2022
Cited by 1 | Viewed by 2017
Abstract
To lower maintenance costs and improve a metro company’s competitiveness, this research came up with an innovative technique using a considering sensitivity and analytic hierarchy process (CSAHP). Along with interviews with managers and workers at the Taipei Rapid Transit Corporation, this study was [...] Read more.
To lower maintenance costs and improve a metro company’s competitiveness, this research came up with an innovative technique using a considering sensitivity and analytic hierarchy process (CSAHP). Along with interviews with managers and workers at the Taipei Rapid Transit Corporation, this study was able to undertake quantitative analysis. To determine which subsystems and metro lines should be prioritized for outsourcing based on the CSAHP framework, we used the criterium decision plus (CDP) program. This research adds to the existing body of knowledge by advancing the existing analytic hierarchy process (AHP) technique and recommending the CSAHP strategy for assessment. According to the findings, the power supply system was the most in need of outsourcing, followed by air conditioning, firefighting, and elevator systems. When considering which of the four metro lines to outsource first, the blue line came out on top, followed by the red, green, and brown lines. By prioritizing the outsourcing of the power supply system as a result of this research, the Taipei Rapid Transit Corporation may cut the system’s maintenance expenditures from USD 1.57 million to USD 1.33 million, saving 15% on maintenance costs. Applying these findings can improve the economic benefits of outsourced maintenance for the Taipei Rapid Transit Corporation. Full article
(This article belongs to the Special Issue Advances in Smart Industrial Engineering Techniques for Optimizing and Controlling Processes)
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Review

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20 pages, 894 KiB  
Review
Capability Indices for Digitized Industries: A Review and Outlook of Machine Learning Applications for Predictive Process Control
by Jan Mayer and Roland Jochem
Processes 2024, 12(8), 1730; https://doi.org/10.3390/pr12081730 - 16 Aug 2024
Cited by 2 | Viewed by 1495
Abstract
Leveraging machine learning applications for predictive process control signifies a decisive advancement in manufacturing quality management, transitioning from traditional descriptive to predictive capability indices. This review highlights the growing importance of predictive process control, essential for quality assurance and the dynamic adaptability of [...] Read more.
Leveraging machine learning applications for predictive process control signifies a decisive advancement in manufacturing quality management, transitioning from traditional descriptive to predictive capability indices. This review highlights the growing importance of predictive process control, essential for quality assurance and the dynamic adaptability of production lines, which is paramount in satisfying stringent quality standards and evolving consumer demands. The investigation into the integration of comprehensive sensor networks and sophisticated algorithmic analytics enriches continuous improvement strategies, markedly enhancing the accuracy and efficiency of production quality monitoring and control mechanisms. By moving beyond the limits of statistical process control to predictive methods enabled by machine learning algorithms, the study presents a transformative leap in manufacturing processes. The presented findings illustrate the critical role of predictive algorithms in navigating the complexities of process variability, thereby ensuring consistent adherence to established quality specifications. This approach not only facilitates immediate and accurate product quality categorization, increasing overall operational efficiency, but also equips manufacturers to swiftly respond to the variable nature of manufacturing requirements. Furthermore, this research delves into the multifaceted impacts of predictive process control on the manufacturing ecosystem. The ability to predict process quality decrease before it occurs, the optimization of resource allocation, and the anticipation of production bottlenecks before they impact output are among the notable benefits of this technological evolution. These developments to predictive process control is instrumental in propelling the manufacturing industry toward a more agile, sustainable, and customer-centric future. This shift not only complements the industry’s drive toward comprehensive digitization but also promises significant strides in achieving superior process improvements and maintaining a competitive edge on the global market. Full article
(This article belongs to the Special Issue Advances in Smart Industrial Engineering Techniques for Optimizing and Controlling Processes)
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23 pages, 3068 KiB  
Review
A Systematic Review of Multi-Objective Evolutionary Algorithms Optimization Frameworks
by Andrei Pătrăușanu, Adrian Florea, Mihai Neghină, Alina Dicoiu and Radu Chiș
Processes 2024, 12(5), 869; https://doi.org/10.3390/pr12050869 - 26 Apr 2024
Cited by 5 | Viewed by 5858
Abstract
The study of evolutionary algorithms (EAs) has witnessed an impressive increase during the last decades. The need to explore this area is determined by the growing request for design and the optimization of more and more engineering problems in society, such as highway [...] Read more.
The study of evolutionary algorithms (EAs) has witnessed an impressive increase during the last decades. The need to explore this area is determined by the growing request for design and the optimization of more and more engineering problems in society, such as highway construction processes, food and agri-technologies processes, resource allocation problems, logistics and transportation systems, microarchitectures, suspension systems optimal design, etc. All of these matters refer to specific highly computational problems with a huge design space, hence the obvious need for evolutionary algorithms and frameworks, or platforms that allow for the implementing and testing of such algorithms and methods. This paper aims to comparatively analyze the existing software platforms and state-of-the-art multi-objective optimization algorithms and make a review of what features exist and what features might be included next as further developments in such tools, from a researcher’s perspective. Additionally, it is essential for a framework to be easily extendable with new types of problems and optimization algorithms, metrics and quality indicators, genetic operators or specific solution representations and results analysis and comparison features. After presenting the most relevant existing features in these types of platforms, we suggest some future steps and the developments we have been working on. Full article
(This article belongs to the Special Issue Advances in Smart Industrial Engineering Techniques for Optimizing and Controlling Processes)
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