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Application of Mathematical Methods in Industrial Engineering and Management: 2nd...
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Application of Mathematical Methods in Industrial Engineering and Management: 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 1412

Special Issue Editor

Prof. Dr. Yi-Kuei Lin

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu 300, Taiwan
Interests: network reliability; systems quality management; process reengineering; service science & management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this Special Issue, we focus on applications of mathematical methods in industrial engineering and management and invite related high-quality papers covering theory and practice. Developing and applying mathematical methods, such as operations research, stochastic processes, and statistics, in order to measure the performance and solve engineering problems associated with real systems are truly crucial tasks. Several practical systems can be involved in related problems, including computer, logistic, and production systems. The management implications, improvements, and decisions made based on system performance evaluation are all within the scope.

Hence, we invite related high-quality papers that solve practical problems from industrial engineering and management to mathematical approaches. Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Potential topics include, but are not limited to, the following:

  • Operations research applications;
  • Quality measurement;
  • Performance evaluation;
  • Applications in production, manufacturing, and logistics;
  • Statistics in production, manufacturing, and logistics;
  • Reliability engineering;
  • Decision support;
  • Application in computing and artificial intelligence;
  • Information management;
  • Management science;
  • Soft computing on production, manufacturing, and logistics.

Prof. Dr. Yi-Kuei Lin
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. Mathematics 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 2600 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

  • operation research applications
  • statistics applications
  • optimization
  • mathematical modeling
  • reliability
  • decision analysis
  • artificial intelligence applications

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Published Papers (2 papers)

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Research

20 pages, 8696 KiB  
Article
Reliability Modeling of Systems with Undetected Degradation Considering Time Delays, Self-Repair, and Random Operating Environments
by Hoang Pham
Mathematics 2024, 12(18), 2916; https://doi.org/10.3390/math12182916 - 19 Sep 2024
Viewed by 465
Abstract
In some settings, systems may not fail completely but instead undergo performance degradation, leading to reduced efficiency. A significant concern arises when a system transitions into a degraded state without immediate detection, with the degradation only becoming apparent after an unpredictable period. Undetected [...] Read more.
In some settings, systems may not fail completely but instead undergo performance degradation, leading to reduced efficiency. A significant concern arises when a system transitions into a degraded state without immediate detection, with the degradation only becoming apparent after an unpredictable period. Undetected degradation can result in failures with significant consequences. For instance, a minor crack in an oil pipeline might go unnoticed, eventually leading to a major leak, environmental harm, and costly cleanup efforts. Similarly, in the nuclear industry, undetected degradation in reactor cooling systems could cause overheating and potentially catastrophic failure. This paper focuses on reliability modeling for systems experiencing degradation, accounting for time delays associated with undetected degraded states, self-repair mechanisms, and varying operating environments. The paper presents a reliability model for degraded, time-dependent systems, incorporating various aspects of degradation. It first discusses the model assumptions and formulation, followed by numerical results obtained from system modeling using the developed program. Various scenarios are illustrated, incorporating time delays and different parameter values. Through computational analysis of these complex systems, we observe that the probability of the system being in the undetected degraded state tends to stabilize shortly after the initial degradation begins. The model is valuable for predicting and establishing an upper bound on the probability of the undetected, degraded state and the system’s overall reliability. Finally, the paper outlines potential avenues for future research. Full article
(This article belongs to the Special Issue Application of Mathematical Methods in Industrial Engineering and Management: 2nd Edition)
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30 pages, 2859 KiB  
Article
Predictive Resilience Modeling Using Statistical Regression Methods
by Priscila Silva, Mariana Hidalgo, Mindy Hotchkiss, Lasitha Dharmasena, Igor Linkov and Lance Fiondella
Mathematics 2024, 12(15), 2380; https://doi.org/10.3390/math12152380 - 31 Jul 2024
Viewed by 741
Abstract
Resilience describes the capacity of systems to react to, withstand, adjust to, and recover from disruptive events. Despite numerous metrics proposed to quantify resilience, few studies predict these metrics or the restoration time to nominal performance levels, and these studies often focus on [...] Read more.
Resilience describes the capacity of systems to react to, withstand, adjust to, and recover from disruptive events. Despite numerous metrics proposed to quantify resilience, few studies predict these metrics or the restoration time to nominal performance levels, and these studies often focus on a single domain. This paper introduces three methods to model system performance and resilience metrics, which are applicable to various engineering and social science domains. These models utilize reliability engineering techniques, including bathtub-shaped functions, mixture distributions, and regression analysis incorporating event intensity covariates. Historical U.S. job loss data during recessions are used to evaluate these approaches’ predictive accuracy. This study computes goodness-of-fit measures, confidence intervals, and resilience metrics. The results show that bathtub-shaped functions and mixture distributions accurately predict curves possessing V, U, L, and J shapes but struggle with W and K shapes involving multiple disruptions or sudden performance drops. In contrast, covariate-based models effectively track all curve types, including complex W and K shapes, like the successive shocks in the 1980 U.S. recession and the sharp decline in the 2020 U.S. recession. These models achieve a high predictive accuracy for future performance and resilience metrics, evidenced by the low sum of square errors and high adjusted coefficients of determination. Full article
(This article belongs to the Special Issue Application of Mathematical Methods in Industrial Engineering and Management: 2nd Edition)
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