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Cutting-Edge Datasets and Algorithms for Enhancing Industrial Processes and Supply...
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Cutting-Edge Datasets and Algorithms for Enhancing Industrial Processes and Supply Chain Optimization

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: 30 April 2025 | Viewed by 3652

Special Issue Editors

Prof. Dr. Iván Pérez-Olguín

E-Mail Website
Guest Editor
Institute of Engineering and Technology, Department of Industrial and Manufacturing Engineering, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
Interests: lean manufacturing; design optimization; robust optimization; multicriteria decision making; operations research; stochastic modeling
Dr. Luis Carlos Méndez González

E-Mail Website
Guest Editor
Institute of Engineering and Technology, Department of Industrial and Manufacturing Engineering, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
Interests: multi-objective optimization; statistical techniques; machine and deep learning; metaheuristics; stochastics process; artificial intelligence and fuzzy techniques; reliability
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis Alberto Rodríguez-Picón

E-Mail Website
Guest Editor
Institute of Engineering and Technology, Department of Industrial and Manufacturing Engineering, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
Interests: stochastic modeling; degradation processes; design of experiments; reliability engineering; multivariate statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the latest advancements in the use of cutting-edge datasets and algorithms to optimize industrial processes and supply chain management. It aims to bring together researchers and practitioners to share insights, methodologies, and case studies that demonstrate the transformative potential of data-driven approaches in these fields. The issue will explore how innovative data utilization and algorithmic strategies can enhance efficiency, reduce costs, and improve overall performance in industrial and supply chain operations.

Potential topics:

  • Advanced Datasets for Industrial Applications;
  • Data preprocessing and integration techniques;
  • Case studies on the use of datasets in manufacturing and supply chains;  
  • Machine learning and AI algorithms for industrial processes;
  • Optimization algorithms for supply chain management;
  • Predictive maintenance and quality control algorithm;
  • Modeling and analysis of stochastic processes;
  • Applications of stochastic optimization in production planning;
  • Risk management and uncertainty in supply chain;
  • End-to-end supply chain optimization strategies;
  • Inventory management and demand forecasting;
  • Logistics and transportation optimization;  
  • Successful implementations of data-driven strategies.

This Special Issue invites original research papers, reviews, and case studies that provide new insights and practical solutions for enhancing industrial and supply chain process optimization through the use of advanced datasets and algorithms.

Prof. Dr. Iván Pérez-Olguín
Dr. Luis Carlos Méndez González
Prof. Dr. Luis Alberto Rodríguez-Picón
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. Data 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 1600 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

  • industrial processes
  • supply chain management
  • stochastic optimization
  • predictive maintenance
  • inventory management
  • demand forecasting
  • production planning
  • uncertainty management
  • data processing
  • logistics optimization
  • transportation optimization
  • quality control
  • advanced datasets

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (3 papers)

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Research

25 pages, 1900 KiB  
Article
Data-Driven Scheduling Optimization for SMT Lines Using SMD Reel Commonality
by Jorge Quijano, Nohemi Torres Cruz, Leslie Quijano-Quian, Eduardo Rafael Poblano-Ojinaga and Salvador Anacleto Noriega Morales
Data 2025, 10(2), 16; https://doi.org/10.3390/data10020016 - 29 Jan 2025
Viewed by 357
Abstract
Optimizing production efficiency in Surface-Mount Technology (SMT) manufacturing is a critical challenge, particularly in high-mix environments where frequent product changeovers can lead to significant downtime. This study presents a scheduling algorithm that minimizes changeover times on SMT lines by leveraging the commonality of [...] Read more.
Optimizing production efficiency in Surface-Mount Technology (SMT) manufacturing is a critical challenge, particularly in high-mix environments where frequent product changeovers can lead to significant downtime. This study presents a scheduling algorithm that minimizes changeover times on SMT lines by leveraging the commonality of Surface-Mount Device (SMD) reel part numbers across product Bills of Materials (BOMs). The algorithm’s capabilities were demonstrated through both simulated datasets and practical validation trials, providing a comprehensive evaluation framework. In the practical implementation, the algorithm successfully aligned predicted and measured changeover times, highlighting its applicability and accuracy in operational settings. The proposed approach integrates heuristic and optimization techniques to identify scheduling strategies that not only minimize reel changes but also support production scalability and operational flexibility. This framework offers a robust solution for optimizing SMT workflows, enhancing productivity, and reducing resource inefficiencies in both greenfield projects and established manufacturing environments. Full article
(This article belongs to the Special Issue Cutting-Edge Datasets and Algorithms for Enhancing Industrial Processes and Supply Chain Optimization)
16 pages, 3695 KiB  
Article
Parallel Simplex, an Alternative to Classical Experimentation: A Case Study
by Francisco Zorrilla Briones, Inocente Yuliana Meléndez Pastrana, Manuel Alonso Rodríguez Morachis and José Luís Anaya Carrasco
Data 2024, 9(12), 147; https://doi.org/10.3390/data9120147 - 10 Dec 2024
Viewed by 702
Abstract
Experimentation is a strong methodology that improves and optimizes processes. Nevertheless, in many cases, real-life dynamics of production demands and other restrictions inhibit the use of these methodologies because their use implies stopping production, generating scrap, jeopardizing demand accomplishments, and other problems. Proposed [...] Read more.
Experimentation is a strong methodology that improves and optimizes processes. Nevertheless, in many cases, real-life dynamics of production demands and other restrictions inhibit the use of these methodologies because their use implies stopping production, generating scrap, jeopardizing demand accomplishments, and other problems. Proposed here is an alternative methodology to search for the best process variable levels and optimize the response of the process without the need to stop production. This algorithm is based on the principles of the Variable Simplex developed by Nelder and Mead and the continuous iterative process of EVOPS developed by Box, which is then modified as a simplex by Spendley. It is named parallel simplex because it searches for the best response with three independent Simplexes searching for the same response at the same time. The algorithm was designed for three simplexes of two input variables each. The case study documented shows that it is efficient and effective. Full article
(This article belongs to the Special Issue Cutting-Edge Datasets and Algorithms for Enhancing Industrial Processes and Supply Chain Optimization)
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37 pages, 28638 KiB  
Article
Characterization and Dataset Compilation of Torque–Angle Curve Behavior for M2/M3 Screws
by Iván Juan Carlos Pérez-Olguín, Consuelo Catalina Fernández-Gaxiola, Luis Alberto Rodríguez-Picón and Luis Carlos Méndez-González
Data 2024, 9(10), 115; https://doi.org/10.3390/data9100115 - 6 Oct 2024
Viewed by 1299
Abstract
This research explores the torque–angle behavior of M2/M3 screws in automotive applications, focusing on ensuring component reliability and manufacturing precision within the recommended assembly specification limits. M2/M3 screws, often used in tight spaces, are susceptible to issues like stripped threads and inconsistent torque, [...] Read more.
This research explores the torque–angle behavior of M2/M3 screws in automotive applications, focusing on ensuring component reliability and manufacturing precision within the recommended assembly specification limits. M2/M3 screws, often used in tight spaces, are susceptible to issues like stripped threads and inconsistent torque, which can compromise safety and performance. The study’s primary objective is to develop a comprehensive dataset of torque–angle measurements for these screws, facilitating the analysis of key parameters such as torque-to-seat, torque-to-fail, and process windows. By applying Gaussian curve fitting and Gaussian process regression, the research models and simulates torque behavior to understand torque dynamics in small fasteners and remarks on the potential of statistical methods in torque analysis, offering insights for improving manufacturing practices. As a result, it can be concluded that the proposed stochastics methodologies offer the benefit of fail-to-seat ratio improvement, allow inference, reduce the sample size needed in incoming test studies, and minimize the number of destructive test samples needed. Full article
(This article belongs to the Special Issue Cutting-Edge Datasets and Algorithms for Enhancing Industrial Processes and Supply Chain Optimization)
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