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Applied Sciences
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Decision Support Systems: Novel Applications and Future Perspectives
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Decision Support Systems: Novel Applications and Future Perspectives

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 5957

Special Issue Editor

Dr. Konrad Kulakowski

E-Mail Website
Guest Editor
Department Of Applied Computer Science, AGH University of Krakow, 30-059 Kraków, Poland
Interests: decision-making methods; pairwise comparisons; decision inconsistency; algorithms; parallel programming; computational complexity; intelligent control systems and their applications in robotics; collective intelligence; multi-agent architectures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Decision support systems are increasingly present in the everyday life of many people. Some operate invisibly when we drive a car, use the Internet, book a plane ticket or look for the best place to spend our holidays. Others explicitly require our involvement and determination of our preferences. Decision support systems can use machine learning techniques, big data, or statistical analysis. They can also process expert data to provide recommendations (often in the form of ranking alternatives).

While this particular Special Issue remains open to various types of contributions that deal with decision support systems, papers on multi-criteria decision-making (MCDM/MCDA) would be of specific interest. In addition, submissions that show decision support systems in various applications such as information technology, telecommunications, automation and robotics, medicine are welcome.

Dr. Konrad Kulakowski
Guest Editor

Manuscript Submission Information

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Keywords

  • decision-making methods
  • pairwise comparisons
  • decision inconsistency
  • algorithms
  • parallel programming
  • computational complexity
  • intelligent control systems and their applications in robotics
  • collective intelligence
  • multi-agent architectures

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Published Papers (4 papers)

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Research

12 pages, 403 KiB  
Article
Application of Reinforcement Learning in Decision Systems: Lift Control Case Study
by Mateusz Wojtulewicz and Tomasz Szmuc
Appl. Sci. 2024, 14(2), 569; https://doi.org/10.3390/app14020569 - 9 Jan 2024
Cited by 2 | Viewed by 1200
Abstract
This study explores the application of reinforcement learning (RL) algorithms to optimize lift control strategies. By developing a versatile lift simulator enriched with real-world traffic data from an intelligent building system, we systematically compare RL-based strategies against well-established heuristic solutions. The research evaluates [...] Read more.
This study explores the application of reinforcement learning (RL) algorithms to optimize lift control strategies. By developing a versatile lift simulator enriched with real-world traffic data from an intelligent building system, we systematically compare RL-based strategies against well-established heuristic solutions. The research evaluates their performance using predefined metrics to improve our understanding of RL’s effectiveness in solving complex decision problems, such as the lift control algorithm. The results of the experiments show that all trained agents developed strategies that outperform the heuristic algorithms in every metric. Furthermore, the study conducts a comprehensive exploration of three Experience Replay mechanisms, aiming to enhance the performance of the chosen RL algorithm, Deep Q-Learning. Full article
(This article belongs to the Special Issue Decision Support Systems: Novel Applications and Future Perspectives)
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14 pages, 1001 KiB  
Article
New Approach for Process Capability Analysis Using Multivariate Quality Characteristics
by Moath Alatefi, Abdulrahman M. Al-Ahmari and Abdullah Yahia AlFaify
Appl. Sci. 2023, 13(21), 11616; https://doi.org/10.3390/app132111616 - 24 Oct 2023
Cited by 2 | Viewed by 1702
Abstract
The evaluation of manufacturing processes aims to ensure that the processes meet the desired requirements. Therefore, process capability indexes are used to measure the capability of a process to meet customer requirements and/or engineering specifications. However, most of the manufacturing products have more [...] Read more.
The evaluation of manufacturing processes aims to ensure that the processes meet the desired requirements. Therefore, process capability indexes are used to measure the capability of a process to meet customer requirements and/or engineering specifications. However, most of the manufacturing products have more than one quality characteristic (QC), in which case, the multivariate QCs should be evaluated together using a single capability index. The research in this article proposes a methodology for estimating the multivariate process capability index (PCI). First, the dimensions of the multivariate QCs are reduced into a new single variable using the proportion of the process specification region, by comparing each variable datapoint to its specification limits. Moreover, nonnormal data are transformed to normality using a root transformation algorithm. Then, a large data sample is generated using the parameters of the new variable. The generated data are compared to the specification limits to estimate the percent of nonconforming (PNC). Finally, the capability index of a given process datapoints is estimated using the PNC. Accordingly, managerial insights for the implementation of the proposed methodology in real industry are presented. The methodology was assessed by well-known multivariate samples from four different distributions, in which an algorithm was developed for generating these samples with their given correlations. The results show the effectiveness of the proposed methodology for estimating multivariate PCIs. Also, the results from this research outperform the previous published results in most cases. Full article
(This article belongs to the Special Issue Decision Support Systems: Novel Applications and Future Perspectives)
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12 pages, 905 KiB  
Article
Decision-Making Model of Production Data Management for Multi-Quality Characteristic Products in Consideration of Industry 4.0
by Kuen-Suan Chen, Song-Chang Lin, Kuei-Kuei Lai and Wen-Pai Wang
Appl. Sci. 2023, 13(13), 7883; https://doi.org/10.3390/app13137883 - 5 Jul 2023
Cited by 1 | Viewed by 957
Abstract
According to numerous studies, various parts processed by machine tools usually have multiple-quality characteristics at the same time. Moreover, the process capability index is a handy and useful tool for assessing various quality characteristics. In order to assist downstream customers in evaluating their [...] Read more.
According to numerous studies, various parts processed by machine tools usually have multiple-quality characteristics at the same time. Moreover, the process capability index is a handy and useful tool for assessing various quality characteristics. In order to assist downstream customers in evaluating their process capabilities, achieve the effect of integrating the production data of the machine tool industry chain, advance the process quality of products, and reduce rework and scrap, we constructed a shared decision-making model of production data management for multi-quality characteristic products on the cloud platform in consideration of Industry 4.0. This model not only can help downstream customers improve the process for quality characteristics with insufficient process precision or accuracy to figure out the optimum machine parameter setting but also can build a better system of repairs and maintenance. At the same time, all downstream customers’ improvement experiences can be gathered to form a knowledge database for improvements and provided to the machine tool industry to set up a complete mechanism of supplier selection, or they can be regarded as a reference for designing superior key components of machine tools, thereby enhancing the product value and industrial competitiveness of machine tools. Full article
(This article belongs to the Special Issue Decision Support Systems: Novel Applications and Future Perspectives)
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33 pages, 1061 KiB  
Article
Robust Additive Value-Based Efficiency Analysis with a Hierarchical Structure of Inputs and Outputs
by Anna Labijak-Kowalska, Miłosz Kadziński and Weronika Mrozek
Appl. Sci. 2023, 13(11), 6406; https://doi.org/10.3390/app13116406 - 24 May 2023
Cited by 3 | Viewed by 1162
Abstract
We introduce a novel methodological framework based on additive value-based efficiency analysis. It considers inputs and outputs organized in a hierarchical structure. Such an approach allows us to decompose the problem into manageable pieces and determine the analyzed units’ strengths and weaknesses. We [...] Read more.
We introduce a novel methodological framework based on additive value-based efficiency analysis. It considers inputs and outputs organized in a hierarchical structure. Such an approach allows us to decompose the problem into manageable pieces and determine the analyzed units’ strengths and weaknesses. We provide robust outcomes by analyzing all feasible weight vectors at different hierarchy levels. The analysis concerns three complementary points of view: distances to the efficient unit, ranks, and pairwise preference relations. For each of them, we determine the exact extreme results and the distribution of probabilistic results. We apply the proposed method to a case study concerning the performance of healthcare systems in sixteen Polish voivodeships (provinces). We discuss the results based on the entire set of factors (the root of the hierarchy) and three subcategories. They concern health improvement of inhabitants, efficient financial management, and consumer satisfaction. Finally, we show the practical conclusions that can be derived from the hierarchical decomposition of the problem and robustness analysis. Full article
(This article belongs to the Special Issue Decision Support Systems: Novel Applications and Future Perspectives)
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