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Systems
Special Issues
Data-Driven Decision-Making Models with Their Applications in Various Industries
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Data-Driven Decision-Making Models with Their Applications in Various Industries

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Engineering".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 5349

Special Issue Editors

Dr. Zhen-Song Chen

E-Mail Website
Guest Editor
Department of Engineering Management, School of Civil Engineering, Wuhan University, Wuhan 430072, China
Interests: engineering management; multi-objective optimization, decision support; computational semantics analysis; group decision making; computing with words
Special Issues, Collections and Topics in MDPI journals
Dr. Lesheng Jin

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Guest Editor
School of Business, Nanjing Normal University, Nanjing 210023, China
Interests: decision making; information fusion; uncertainty modeling; soft computing; computational intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Rosa M. Rodriguez

grade E-Mail Website
Guest Editor
Department of Computer Science, University of Jaén, 23071 Jaén, Spain
Interests: linguistic preference modelling; fuzzy decision making; decision support system; computing with words
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis Martínez López

E-Mail Website
Guest Editor
Department of Computer Science, University of Jaén, 23071 Jaén, Spain
Interests: computational intelligence; fuzzy decision making; recommender systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Witold Pedrycz

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2E1, Canada
Interests: fuzzy set theory; pattern clustering; learning (artificial intelligence); decision making; granular
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advances in the internet technology, Internet of Things, cloud computing, Big Data, and artificial intelligence have profoundly changed people’s lifestyles and become essential elements in everyday decision-making activities for individuals and organizations. With the accumulation of large volumes of data to support people’s decision-making efforts, Big Data offers a tremendous opportunity in the transformation of today’s decision-making paradigm to smart decision making. Unfortunately, it is usually the case that people exhibit limited capabilities to process and analyze large volumes of data. In this sense, people may be used to making decisions based on relevant knowledge, experience, and wisdom involved in historical data, which suffers from bias and errors for individual subjects. More and more people and organizations seek help from reliable models and algorithms that deal with large volumes of data to generate efficient and effective decision support. This has fundamentally prompted the recent advancement of data-driven decision making (DDDM), which has become central research trend in decision analysis as it is backed up by massive amounts of data available and has lower requirements for people’s knowledge and experiences. For the past years, DDDM has been widely applied in medical diagnosis, financial risk prediction, governance in public affairs, landslide susceptibility prediction, travel mode choice, safe operation of wastewater treatment process, etc. Therefore, it is pivotal to explore new DDDM models that are reasonably designed and constructed for collective intelligence and interdependent decision making.

This Special Issue seeks contributions that establish a scholarly foundation for data-driven decision-making models and algorithms to create reliable decision support and solid applications of such new models and algorithms in various industries. The contributions will be presented to decision analysis researchers and practitioners in various industries. Papers are being sought in the following areas (but not limited to):

  • New methods for data-driven decision making (DDDM);
  • Data sampling, aggregation, and anomaly detection for DDDM;
  • Data quality assessment, enforcement, and management in DDDM;
  • Empirical studies on DDDM implementation;
  • Real-time data-driven decision-making models;
  • DDDM under high-level uncertainty;
  • Issues and challenges in DDDM with sustainability considerations;
  • State-of-the-art review of DDDM in various industrial contexts;
  • Quality metrics for evaluating DDDM;
  • Multi-sources data fusion for DDDM;
  • Simulations and visualization in DDDM algorithm design;
  • DDDM in new industrial applications (e.g., health engineering; construction management, digital transformation with industry 4.0/5.0), etc. 

Dr. Zhen-Song Chen
Dr. Lesheng Jin
Dr. Rosa M. Rodriguez
Prof. Dr. Luis Martínez López
Prof. Dr. Witold Pedrycz
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. Systems 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.

Published Papers (3 papers)

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Research

20 pages, 2285 KiB  
Article
A Maturity Model for Diagnosing the Capabilities of Smart Factory Solution Suppliers and Its Pilot Application
by Cheol-Won Cha, Jeongcheol Lee, Sungbum Jun, Keumseok Kang and Tai-Woo Chang
Systems 2023, 11(12), 569; https://doi.org/10.3390/systems11120569 - 06 Dec 2023
Viewed by 1526
Abstract
For the successful and sustainable deployment and diffusion of smart factories, both the capabilities of the adopters who operate the factories and the capabilities of the suppliers who supply information technology and equipment play very important roles. However, since the existing models for [...] Read more.
For the successful and sustainable deployment and diffusion of smart factories, both the capabilities of the adopters who operate the factories and the capabilities of the suppliers who supply information technology and equipment play very important roles. However, since the existing models for diagnosing the capabilities of smart factories are mainly focused on evaluating the capabilities of the manufacturing companies themselves, such as technological capabilities and digital transformation, there are not many models that diagnose the supply capabilities of suppliers from the perspective of demand companies. Unlike models that diagnose the level of smart factories, when diagnosing the capabilities of suppliers, various factors such as supply experience and management capabilities must be comprehensively evaluated in addition to the capabilities of the company itself. Therefore, this study proposes a new model to diagnose the capabilities of suppliers from the perspective of adopters who want to build smart factories and verifies the validity of the model by applying the model for a pilot diagnosis for 32 suppliers. In addition, based on the survey results obtained from both adopters and suppliers participating in the pilot diagnoses, this study proposes an institutionalization plan for capability diagnosis. Full article
(This article belongs to the Special Issue Data-Driven Decision-Making Models with Their Applications in Various Industries)
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11 pages, 1650 KiB  
Article
AatMatch: Adaptive Adversarial Training in Semi-Supervised Learning Based on Data-Driven Decision-Making Models
by Kuan Li, Qianzhi Lian, Can Gao and Fuyong Zhang
Systems 2023, 11(5), 256; https://doi.org/10.3390/systems11050256 - 18 May 2023
Viewed by 1097
Abstract
Data-driven decision-making is the process of using data to inform your decision-making process and validate a course of action before committing to it. The quality of unlabeled data in real-world scenarios presents challenges for semi-supervised learning. Effectively leveraging unlabeled data for learning is [...] Read more.
Data-driven decision-making is the process of using data to inform your decision-making process and validate a course of action before committing to it. The quality of unlabeled data in real-world scenarios presents challenges for semi-supervised learning. Effectively leveraging unlabeled data for learning is challenging due to the need for labeled information, while the scarcity of labeled data requires efficient and flexible data augmentation methods. To address these challenges, this paper proposes the AatMatch algorithm, which uses a momentum model, coarse learning, and adversarial training to generate adversarial examples for different classes. The algorithm sets the threshold for generating pseudo-labels and reinforces the results with adversarial perturbations based on evaluation results. In addition, a more refined learning strategy for unlabeled data is adjusted by setting adaptive weights based on the confidence of each unlabeled data point, thereby mitigating the adverse effects of low-confidence unlabeled data on the model. Experimental evaluations on several datasets, including CIFAR-10, CIFAR-100, and SVHN, demonstrate the effectiveness of the proposed AatMatch algorithm in semi-supervised learning. Specifically, the algorithm achieves the lowest error rates for multiple scenarios on these datasets. Full article
(This article belongs to the Special Issue Data-Driven Decision-Making Models with Their Applications in Various Industries)
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17 pages, 1093 KiB  
Article
Bidding Evaluation and Contractor Selection Using Balance Index Model and Comprehensive Input Efficiency Based on Data Envelopment Analysis
by Xun Liu, Siyu Chen, Zhenhan Ding and Bixiao Xu
Systems 2023, 11(5), 245; https://doi.org/10.3390/systems11050245 - 14 May 2023
Cited by 3 | Viewed by 1536
Abstract
In order to ensure a smooth construction project, it is necessary to select an appropriate contractor. However, traditional bid evaluation methods are highly subjective in determining weights. Data envelopment analysis (DEA), a comprehensive bid evaluation method that considers multiple factors, was introduced to [...] Read more.
In order to ensure a smooth construction project, it is necessary to select an appropriate contractor. However, traditional bid evaluation methods are highly subjective in determining weights. Data envelopment analysis (DEA), a comprehensive bid evaluation method that considers multiple factors, was introduced to reduce subjectivity and provide a simple yet comprehensive method for evaluating bids. Based on the existing cross-evaluation and balance index models, this research proposed a new DEA ranking model—the comprehensive input efficiency model, as well as its specific application steps. Additionally, a case study on selecting contractors for a water engineering project was presented to demonstrate the effectiveness of this model. The results indicated that the comprehensive input efficiency model could achieve the same ranking function as the balance index and was suitable for assessing bidders’ relative efficiency. Moreover, the comprehensive input efficiency model proposed in this research is more simplified. Thus, this research compensates for the drawbacks of the existing comprehensive evaluation models in that the bid evaluation process is cumbersome, thereby extending the research on DEA methods in bid evaluation. Additionally, the model provides tenderers with a more efficient and effective bid evaluation method to select the most appropriate contractor. In the future, research may be conducted to apply DEA to other types of projects, including service projects, real estate, and consulting services. Full article
(This article belongs to the Special Issue Data-Driven Decision-Making Models with Their Applications in Various Industries)
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