Integration of Z-numbers and Bayesian decision theory: A hybrid approach to decision making under uncertainty and imprecision

Marhamati, Nina; Buxton, Elham Khorasani; Rahimi, Shahram

doi:10.1016/j.asoc.2018.07.053

Cited by 21 publications

(4 citation statements)

References 25 publications

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“…In the marine environment, data are collected from a variety of sources, including oceans, seas, coastlines, and other saltwater bodies. There is a critical need for these data in order to understand and manage marine ecosystems, oceanographic processes, weather patterns, as well as a wide range of scientific, environmental, and commercial activities [21,22].…”

Section: Marine Datamentioning

confidence: 99%

Advancements and Challenges in Deep Learning-Driven Marine Data Assimilation: A Comprehensive Review

Ma,

Zhang,

Zhang

et al. 2023

CRPASE

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The acquisition and assimilation of high-quality data are fundamental for predictive model development across various domains. In the maritime realm, superior marine data fuels advancements in ship industry innovation, offshore clean energy initiatives, and marine engineering. Recent strides in employing deep learning methodologies have significantly improved data assimilation processes, raising the quality of derived datasets. This review meticulously examines deep learning-driven marine data assimilation, dissecting its challenges, identifying research gaps, and outlining future trajectories. This study employs Citespace's scientometric survey to comprehensively visualize and analyze the constituent elements within the literature, as well as to scrutinize the present state of research across pertinent fields, thereby providing an in-depth exploration and critical assessment of the scholarly landscape. Using bibliometric analysis, keyword exploration, and discipline classification, prevailing research patterns and emerging focal points are dissected. An insightful exploration into marine data nuances illuminates inherent challenges. Moreover, a comparative assessment of diverse algorithmic applications offers insights into their efficacy within this specialized domain. Culminating in a meticulous synthesis, this paper reveals pivotal developmental constraints in marine data assimilation, providing guidance for advancements across multifaceted dimensions in this field.

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Section: Marine Datamentioning

confidence: 99%

Advancements and Challenges in Deep Learning-Driven Marine Data Assimilation: A Comprehensive Review

Ma,

Zhang,

Zhang

et al. 2023

CRPASE

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“…Generally, there are several categories of epistemic uncertainty analysis methods, for example, subjective probability theory, fuzzy set theory, evidence theory, and interval theory. [15][16][17][18][19] However, these methods are not always applicable to reliability analysis. The subjective probability theory cannot always provide sufficient decision support because the Bayesian results are very sensitive to prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…However, previous studies did not consider epistemic uncertainty in the reliability index determination, and only a few researchers proposed reliability metrics and analysis methods to quantify the epistemic uncertainty of a system. Generally, there are several categories of epistemic uncertainty analysis methods, for example, subjective probability theory, fuzzy set theory, evidence theory, and interval theory 15–19 . However, these methods are not always applicable to reliability analysis.…”

Section: Introductionmentioning

confidence: 99%

Belief reliability index determination method based on group decision‐making

Gao

et al. 2023

Quality & Reliability Eng

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For newly developing systems, the determination of the reliability index is an important task in the system demonstration process that determines both the system design goal and the verification requirement. However, due to the insufficient data at the beginning of the system life cycle, the problem of epistemic uncertainty cannot be ignored. Researchers have proposed several belief reliability metrics and analysis methods to quantify the epistemic uncertainty of a system, but to the best knowledge of the authors, no relevant studies have been performed on system reliability index determination. In this paper, we propose a belief reliability index determination method based on group decision‐making. Using a similar system‐based reliability index determination method, this method comprehensively considers the demands of users and the capabilities of contractors. In addition, the Delphi method and Hadamard multiplication convex combination are combined to effectively solve the epistemic uncertainty quantification problem. The Delphi method is used to find the conflicts caused by multi‐expert scoring, and the Hadamard multiplication convex combination can integrate the scoring matrix of all experts, which makes the results more reasonable. Finally, a Low‐Earth‐orbit (LEO) satellite communication system is used to verify the effectiveness of our belief reliability index determination method.

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“…A simple Z-number is shown in Figure 2. A number of articles that rely on the above definition of Z-numbers, such as the ZBWM method for evaluating the supplier [69], a combination of Z-numbers and Bayesian decision theory [70], Z-VIKOR to make decisions [71], SMAA model for decision support [71], DEA and neural network to assess supply chain sustainability [72], sample selection of portfolio [73], and sustainable supplier selection by a possibilistic hierarchical model in the context of Z-information [74], have been studied.…”

mentioning

confidence: 99%

Ranking Sustainable Projects through an Innovative Hybrid DEMATEL‐VIKOR Decision‐Making Approach Using Z‐Number

Akhavein

RezaHoseini

Ramezani

et al. 2021

Advances in Civil Engineering

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According to the heavy reliance of economic growth on environmental and social matters, sustainable development has turned into one of the main strategies of the organizations associated with selecting the project basket. Moreover, market conditions, rapid global changes in several aspects, uncertainty in intellectual judgments of the experts, and many other factors have increased uncertainty in problems of this kind. Accordingly, the main objective of this paper is to develop a mixed decision-making approach (DEMATEL-VIKOR) with the aim of ranking and evaluating suggested projects considering sustainability indices in a “mass production and infrastructural” company under uncertainty, while also taking reliability (Z-number information) into account. Given the existing uncertainty in the opinions of experts and previous data, fuzzy approach and Z-number information have been employed for weighting the filtered sustainability indices that are aligned with the objectives of the considered company (Z-DEMATEL), as well as ranking candidate projects (Z-VIKOR), which contribute to the main innovation of the present research. In addition, for scoring in the Z-number state, a novel definition of linguistic variable (fuzzy linguistic variable considering probabilities) has been put forward. The suggested approach was solved by an expert in two numerical examples in three certain, fuzzy, and Z-number scenarios, the results of which show that different results are obtained for different scenarios. Apart from that, given that Z-number takes the probability of reliance on the opinions of experts into consideration, the more the approach moves from being certain towards the fuzzy state and the Z-number state, the more the results approach the real world, hence yielding more reliable results. Therefore, results obtained from the proposed Z-DEMATEL-VIKOR approach are far more reliable. Moreover, significance weights in the Z-number scenario are 39%, 26%, and 36% in the economic, social, and environmental dimensions, respectively, which is indicative of the importance of social and environmental aspects apart from the economic dimension.

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Integration of Z-numbers and Bayesian decision theory: A hybrid approach to decision making under uncertainty and imprecision

Cited by 21 publications

References 25 publications

Advancements and Challenges in Deep Learning-Driven Marine Data Assimilation: A Comprehensive Review

Advancements and Challenges in Deep Learning-Driven Marine Data Assimilation: A Comprehensive Review

Belief reliability index determination method based on group decision‐making

Ranking Sustainable Projects through an Innovative Hybrid DEMATEL‐VIKOR Decision‐Making Approach Using Z‐Number

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