Applied Statistics in Management Sciences

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 617

Special Issue Editors


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Guest Editor
Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Interests: genetic statistics; simultaneous statistical inference

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Guest Editor
Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Interests: practical application of statistics; quality management and control; inventory management; industrial management

Special Issue Information

Dear Colleagues,

Applied statistics is a type of of data analysis and is devoted to identifying reliable solutions based on analysis and estimation results. This Special Issue seeks high-quality studies focusing on the practice of statistical techniques in enabling the development of scientific decisions in management. We recommend that mathematical modeling, statistical skills, programming, algorithms, deep learning, and simulation are applied to make decisions that are more routed in science. An application example is provided to illustrate the practicality and applicability of the proposed method. Some managerial insights are also discussed. Novel applications from all fronts of management will also be considered. Topics include, but are not limited to, the following:

  • Marketing management;
  • Production management;
  • Inventory management;
  • Quality management and control;
  • Financial management;
  • Operations research;
  • Supply chain management;
  • Education policy;
  • Public health policy;
  • Environment and energy policy.

Prof. Dr. Chia-Ding Hou
Dr. Rung-Hung Su
Guest Editors

Manuscript Submission Information

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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

  • applied statistics
  • management sciences
  • decision-making
  • data analysis
  • deep learning

Published Papers (1 paper)

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Research

17 pages, 5891 KiB  
Article
Lifetime Distribution for a Mixed Redundant System with Imperfect Switch and Components Having Phase–Type Time-to-Failure Distribution
by Myung-Ki Baek and Heungseob Kim
Mathematics 2024, 12(8), 1191; https://doi.org/10.3390/math12081191 - 16 Apr 2024
Viewed by 386
Abstract
Recently, a mixed redundancy was introduced among the redundant design strategies to achieve a more reliable system within the equivalent resources. This study deals with a lifetime distribution for a mixed redundant system with an imperfect fault detector/switch. The lifetime distribution model was [...] Read more.
Recently, a mixed redundancy was introduced among the redundant design strategies to achieve a more reliable system within the equivalent resources. This study deals with a lifetime distribution for a mixed redundant system with an imperfect fault detector/switch. The lifetime distribution model was formulated using a structured continuous Markov chain (CTMC) and considers the time-to-failure (TTF) distribution of a component as a phase-type distribution (PHD). The model’s versatility and practicality are enhanced because the PHD can represent diverse degradation patterns of the components exposed to varied operating environments. The model provides accurate reliability for a mixed redundant system by advancing the approximate reliability function suggested in previous studies. Furthermore, the model would be useful in system design and management because it provides information such as the nth moment of the system’s lifetime distribution. In numerical experiments on some examples, the mixed redundancy was confirmed to devise a more reliable system than the existing active and standby redundancies, and the improvement effect increased as the number of redundant components decreased. The optimal structure for maximizing the expected lifetime of the system changes depends on the reliability of the components and fault detector/switch. Full article
(This article belongs to the Special Issue Applied Statistics in Management Sciences)
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