258 research outputs found
Advances in FUZZY techniques and applications: in occasion of Lofti Zadeh 100 birth anniversary
Advances in FUZZY techniques and applications: in occasion of Lotfi Zadeh 100 birth anniversary. Technological and Economic Development of Economy, 27(2), pp. 280-283
A Multi-objective Location Decision Making Model for Emergency Shelters Giving Priority to Subjective Evaluation of Residents
Earthquake is regarded as the most destructive and terrible disaster among all-natural disasters [1]. Experts agree that immediate emergency evacuation is the safest and most effective response to the earthquake disaster [2]. In the research of emergency evacuation planning, the influence of human subjectivity has gradually attracted researchers’ attention. In this paper, we take the human subjectivity as one of the most important factors for emergency evacuation planning. Based on the preferences of the residents at each demand point for the attributes of every candidate emergency shelter, the subjective score of each candidate emergency shelter is obtained. The preferences of residents will change with the refuge time, so do the weights of residents’ subjective scores of all attributes of candidate emergency shelters. Therefore, we use the subjective score function to describe the change of residents’ evaluations for the emergency shelter over time, and take the average value of subjective scores at all refuge times as the primary basis for location decision making. On these bases, we build a multi-objective location decision making model for emergency shelters giving priority to subjective evaluation of residents. In the model, we consider transfer distance, the efficiency of construction funds and the distribution of people among emergency shelters. Considering fairness, we minimize the standard deviation of the scores and the standard deviation of the transfer distances in the model. This model is applied to a case, which verifies its feasibility and shows that human subjectivity plays an important role in emergency evacuation planning
Fuzzy Logic in Decision Support: Methods, Applications and Future Trends
During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making
Granular computing and optimization model-based method for large-scale group decision-making and its application
In large-scale group decision-making process, some decision makers hesitate among several linguistic terms and cannot compare
some alternatives, so they often express evaluation information
with incomplete hesitant fuzzy linguistic preference relations.
How to obtain suitable large-scale group decision-making results
from incomplete preference information is an important and
interesting issue to concern about. After analyzing the existing
researches, we find that: i) the premise that complete preference
relation is perfectly consistent is too strict, ii) deleting all incomplete linguistic preference relations that cannot be fully completed will lose valid assessment information, iii) semantics given
by decision makers are greatly possible to be changed during the
consistency improving process. In order to solve these issues, this
work proposes a novel method based on Granular computing
and optimization model for large-scale group decision-making,
considering the original consistency of incomplete hesitant fuzzy
linguistic preference relation and improving its consistency without changing semantics during the completion process. An illustrative example and simulation experiments demonstrate the
rationality and advantages of the proposed method: i) semantics
are not changed during the consistency improving process, ii)
completion process does not significantly alter the inherent quality of information, iii) complete preference relations are globally
consistent, iv) final large-scale group decision-making result is
acquired by fusing complete preference relations with different weights
A bibliometric analysis of Economic Research-Ekonomska Istrazivanja (2007–2019)
Economic Research-Ekonomska Istrazivanja is an international journal
in the research field of business and economics and firstly
published in 2007. In this paper, we make a bibliometric analysis
of publications in Economic Research-Ekonomska Istrazivanja from
2007 to 2019. According to Web of Science (WoS), we derive 831
publications in the journal after data pre-processing. First, we
explore characteristics of publications and citations based on
widely recognised bibliometric indicators. Second, we present the
influential countries/regions and influential institutions of publications
in the journal. Next, we illustrate science mapping analysis
according to two visualisation tools that are VOS viewer and
CiteSpace. Specifically, co-citation networks and co-authorship
networks are conducted to analyse connection of items. We generate
bust detection analysis to identify the emerging cited
authors and cited journals. Co-occurrence analysis and timeline
view analysis of keywords are developed to detect the hot topics
and trend of the journal. Finally, we make some discussions about
future challenges of the journal in terms of the above analysis.
This paper helps in objectively understanding the development of
Economic Research-Ekonomska Istrazivanja and provides a valuable
reference for the scholars in business and economics
A multi-channel cross-residual deep learning framework for news-oriented stock movement prediction
Stock market movement prediction remains challenging due to
random walk characteristics. Yet through a potent blend of input
parameters, a prediction model can learn sequential features more
intelligently. In this paper, a multi-channel news-oriented prediction
system is developed to capture intricate moving patterns of
the stock market index. Specifically, the system adopts the temporal
causal convolution to process historical index values due to
its capability in learning long-term dependencies. Concurrently, it
employs the Transformer Encoder for qualitative information
extraction from financial news headlines and corresponding preview
texts. A notable configuration to our multi-channel system is
an integration of cross-residual learning between different channels,
thereby allowing an earlier and closer information fusion. The
proposed architecture is validated to be more efficient in trend
forecasting compared to independent learning, by which channels
are trained separately. Furthermore, we also demonstrate the
effectiveness of involving news content previews, improving the
prediction accuracy by as much as 3.39%
A double interaction-based financing group decisionmaking framework considering uncertain information and inconsistent assessment
Financing group decision-making (FGDM), which is an important
stage of project financing, has unique characteristics: large investments
and long payback horizons. Its evaluation results are likely
to be distorted if we ignore the uncertain information and inconsistent
assessment during the decision-making process. In this
study, we propose a double interaction-based FGDM framework
under uncertain information and inconsistent assessment. We
modify the weight setting of evidence reasoning and aggregation
method of probabilistic linguistic term sets to process the above
two issues. The proposed framework is applied in a detailed case
study analysis to display its effectiveness and stability. We expect
the double interaction-based group decision-making framework
under uncertain information and inconsistent assessment to be a
useful tool to understand FGDM processes
A hesitant fuzzy SMART method based on a new score function for information literacy assessment of teachers
As two powerful and flexible tools for decision-makers (DMs) to
model the complex cognition, the hesitant fuzzy set (HFS) and
hesitant fuzzy linguistic term set (HFLTS) allow DMs to express
their opinions with several possible membership values or linguistic
terms on the objects over each criterion. The aim of this article
is to develop a novel score function of the HFS and HFLTS including
hesitant degree and fuzzy degree information. For this purpose,
the notion of fuzzy degree of the hesitant fuzzy element
(HFE) and hesitant fuzzy linguistic element (HFLE) is introduced
first. Then, considering both the hesitant degree and fuzzy degree
information in expressions, the new score function, namely the
Score-H&FD, is designed. Based on which, we extend the classical
SMART (simple multi-attribute rating technique) method to the
hesitant fuzzy environment. As a result, the hesitant fuzzy SMART
(HF-SMART) method is developed in this article. Afterwards, we
apply our proposed approach to assess and rank several teachers
concerning information literacy. Finally, sensitive analysis and
comparative analysis are carried out. The results show that the
proposed method in this article has substantial advantages and
applicability
Interval Consistency Repairing Method for Double Hierarchy Hesitant Fuzzy Linguistic Preference Relation and Application in the Diagnosis of Lung Cancer
Natural language is more in line with the real thoughts of people
than crisp numbers considering that qualitative language information
is more consistent with the expression habits of experts.
Double hierarchy hesitant fuzzy linguistic preference relation
(DHHFLPR) can be used to express complex linguistic preference
information accurately because the pairwise comparison methods
are more accurate than non-pairwise methods. Consistency
reflects the rationalization of a preference relation and can be
used to judge whether a preference relation is self-contradictory
or not. In this paper, an interval consistency index of DHHFLPR is
developed, which is consisted by the consistency indices of all
double hierarchy linguistic preference relations associated with
the DHHFLPR. Additionally, an average consistency index of
DHHFLPR is given by calculating the average value of the consistency
indices of all double hierarchy linguistic preference relations.
Moreover, we develop a consistency checking and repairing
method for DHHFLPR. Finally, we apply the proposed method
into a practical group decision-making problem that is to identify
the most critical factors in developing lung cancer, and some
comparative analyses involving the connections and differences
among the proposed consistency indices are analysed
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