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Mathematics
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Data Driven Decision-Making under Uncertainty (D3U)
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Special Issue "Data Driven Decision-Making under Uncertainty (D3U)"

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

Deadline for manuscript submissions: closed (15 October 2022) | Viewed by 4203

Special Issue Editors

Dr. Dragan Marinkovic

E-Mail Website
Guest Editor
Department of Structural Analysis, Technical University of Berlin, 10623 Berlin, Germany
Interests: finite element analysis; nano materials; nano technology; materials science; modeling; mathematical modeling; experimentation; ansys; labview
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Samarjit Kar

E-Mail Website
Guest Editor
Department of Mathematics, National Institute of Technology Durgapur, Durgapur, India
Interests: operations research; optimization; soft computing; uncertainty theory; financial modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Dragan Pamučar

E-Mail Website
Guest Editor
Department of Logistics, University of Defence, Belgrade, Pavla Jurišića Šturma 33, 11000 Belgrade, Serbia
Interests: multi-criteria decision making problems; computational intelligence; sustainability Neuro-fuzzy systems; fuzzy; rough and intuitionistic fuzzy set theory; neutrosophic theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Data-driven decision making under uncertainty (D3U) practically means extracting data and information, including from Big Data, to make decisions in areas such as emergency response and healthcare to renewable energy. D3U is driven by the advancements afforded through Industry 4.0 worldwide, and the power of hardware handling offered through Cloud and Fog computing and the like. In times of adverse situations, such as the COVID-19 pandemic, crucial issues such as predictive bed allocation, predicting the spread and the stages of the pandemic and so on, have made D3U an inevitable part of life. It is also thus becoming an ardent necessity to deal with challenges such as uncertainty, vagueness, and hesitation in order to come up with rational decisions.

All nations are also striving to achieve eco-environmental conservation, particularly ISO 14000 and ISO 14001, with industries focusing on sustenance and green habits, calling for the furthering of research in these areas.

In this Special Issue, we welcome both conceptual and empirical, as well as qualitative and quantitative research papers that focus on novel ways of exploring private and public data to derive innovative insights in various domains.

Prof. Dr. Dragan Marinkovic
Prof. Dr. Samarjit Kar
Prof. Dr. Dragan Pamučar
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. 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 2100 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

  • decision aiding models
  • learning models
  • meta-heuristic algorithms
  • optimization models
  • predictive models
  • uncertain modeling
  • MCDM

Published Papers (3 papers)

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Article
A Framework for Project Delivery Systems via Hybrid Fuzzy Risk Analysis: Application and Extension in ICT
by
Mohammad Javad Bidel
,
Hossein Safari
,
Hannan Amoozad Mahdiraji
,
Edmundas Kazimieras Zavadskas
and
Jurgita Antucheviciene
Mathematics 2022, 10(17), 3185; https://doi.org/10.3390/math10173185 - 03 Sep 2022
Viewed by 741
Abstract
One of the essential factors of project success is selecting the proper delivery method. This study aimed to provide a new hybrid decision-making framework to assist project stakeholders in evaluating and selecting the most appropriate Project Delivery System (PDS) and documenting the decision [...] Read more.
One of the essential factors of project success is selecting the proper delivery method. This study aimed to provide a new hybrid decision-making framework to assist project stakeholders in evaluating and selecting the most appropriate Project Delivery System (PDS) and documenting the decision process. For this purpose, the selection factors of PDSs were obtained from a literature review, and critical selection factors were screened based on the fuzzy Delphi method, whereby expert feedback was on Information and Communication Technology (ICT) projects was obtained. Subsequently, the ICT project risks were identified and categorized into six competitive constraints, including time, cost, quality, reputation, value, and scope, and the risk factors were prioritized in each area. Then, the effect of project risks on the decision criteria was investigated using a fuzzy cognitive map (FCM). Finally, the PDSs were ranked through Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS). This article researched a novel multi-layer decision system combining the FCM and FTOPSIS techniques. The decision criteria received their weights from the evaluation of the causal relationships between PDS selection factors and project risks. Thus, PDSs were ranked based on different project characteristics, the opinions of stakeholders, and the effect of project risks on the decision-making process; this increased the likelihood of project success. The results showed that the impact of the most critical project risks on the selection factors was so severe that they changed the weight of the criteria in the decision matrix and, subsequently, changed the ranking of decision options. Full article
(This article belongs to the Special Issue Data Driven Decision-Making under Uncertainty (D3U))
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Article
A Novel Integrating Data Envelopment Analysis and Spherical Fuzzy MCDM Approach for Sustainable Supplier Selection in Steel Industry
by
Thi-Ly Nguyen
,
Phi-Hung Nguyen
,
Hong-Anh Pham
,
Thi-Giang Nguyen
,
Duc-Thinh Nguyen
,
Thi-Hoai Tran
,
Hong-Cham Le
and
Huong-Thuy Phung
Mathematics 2022, 10(11), 1897; https://doi.org/10.3390/math10111897 - 01 Jun 2022
Cited by 7 | Viewed by 1426
Abstract
Supply chain sustainability, which takes environmental, economic, and social factors into account, was recently recognized as a critical component of the supply chain (SC) management evaluation process and known as a multi-criteria decision-making problem (MCDM) that is heavily influenced by the decision-makers. While [...] Read more.
Supply chain sustainability, which takes environmental, economic, and social factors into account, was recently recognized as a critical component of the supply chain (SC) management evaluation process and known as a multi-criteria decision-making problem (MCDM) that is heavily influenced by the decision-makers. While some criteria can be analyzed numerically, a large number of qualitative criteria require expert review in linguistic terms. This study proposes an integration of Data Envelopment Analysis (DEA), spherical fuzzy analytic hierarchy process (SF-AHP), and spherical fuzzy weighted aggregated sum product assessment (SF-WASPAS) to identify a sustainable supplier for the steel manufacturing industry in Vietnam. In this study, both quantitative and qualitative factors are considered through a comprehensive literature review and expert interviews. The first step employs DEA to validate high-efficiency suppliers based on a variety of quantifiable criteria. The second step evaluates these suppliers further on qualitative criteria, such as economic, environmental, and social factors. The SF-AHP was applied to obtain the criteria’s significance, whereas the SF-WASPAS was adopted to identify sustainable suppliers. The sensitivity analysis and comparative results demonstrate that the decision framework is feasible and robust. The findings of this study can assist steel industry executives in resolving the macrolevel supplier selection problem. Moreover, the proposed method can assist managers in selecting and evaluating suppliers more successfully in other industries. Full article
(This article belongs to the Special Issue Data Driven Decision-Making under Uncertainty (D3U))
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Article
Determining Objective Characteristics of MCDM Methods under Uncertainty: An Exploration Study with Financial Data
by
Mahmut Baydaş
and
Dragan Pamučar
Mathematics 2022, 10(7), 1115; https://doi.org/10.3390/math10071115 - 31 Mar 2022
Cited by 7 | Viewed by 1071
Abstract
A major difficulty in comparing and even choosing MCDM methods is the uncertainty of information about the consistent and unique characteristics of the results produced. The objective information content of the final scores produced by MCDM methods and their relevance to real life [...] Read more.
A major difficulty in comparing and even choosing MCDM methods is the uncertainty of information about the consistent and unique characteristics of the results produced. The objective information content of the final scores produced by MCDM methods and their relevance to real life can give us an important idea about them. In this study, first of all, seven MCDM methods with different methodologies were applied to evaluate companies’ financial performance. Then, the obtained MCDM scores were compared using two different objective verification mechanisms. The first validation criterion is the relationship of a MCDM method to real-life rankings (share price). The second criterion is the standard deviation (SD) technique used to discover the objective information content of MCDM final scores. According to the results of this study, PROMETHEE and FUCA definitely outperform other methods in terms of both SD values and strength of correlation with reference real-life rankings. Also, FUCA is methodologically simpler than other methods. However, it produced nearly identical results as the sophisticated PROMETHEE method. Full article
(This article belongs to the Special Issue Data Driven Decision-Making under Uncertainty (D3U))
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