Fuzzy algorithm for decision making in mining engineering

1987 ◽  
Vol 5 (2) ◽  
pp. 149-154 ◽  
Author(s):  
Sukumar Bandopadhyay
Keyword(s):  
Decision Making ◽  
Mining Engineering ◽  
Fuzzy Algorithm

scholarly journals TSUKAMOTO FUZZY METHOD IN DECISSON SUPPORT SYSTEM DESIGN

JURTEKSI ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 83-88
Author(s):  
Arridha Zikra Syah
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
System Design ◽  
Support System ◽  
Support Systems ◽  
Central Office ◽  
Fuzzy Algorithm ◽  
Manual Calculation ◽  
Support Decision Making

Abstract: PT Pinus Merah Abadi is one of the distributor companies in Indonesia engaged in selling snacks such as snacks and wafers with the Nabati brand. Every month the company offers increased incentives for its employees with predetermined conditions or targets. But in the process, the incentive calculation is managed manually using criteria by the General Affair Personnel who then data the results of the manual calculation are sent to the central office to obtain the disbursement of funds. Sometimes the results of these decisions are too rigid. The method used to solve this problem is the RAD method and each stage is adjusted accordingly based on the Tsukamoto fuzzy algorithm. From this study, an application of decision support systems was obtained that could support decision making to increase incentives that were more appropriate in human consideration. Keywords: decison support system design; incentive calculation; tsukamoto method Abstrak: PT Pinus Merah Abadi merupakan salah satu perusahaan distributor di Indonesia yang bergerak di bidang penjualan makanan ringan seperti snack dan wafer dengan merk nabati. Setiap bulan perusahaan menawarkan insentif yang meningkat bagi karyawannya dengan kondisi atau target yang telah ditentukan. Namun dalam prosesnya, perhitungan insentif dikelola secara manual menggunakan kriteria oleh personil urusan umum yang kemudian data hasil perhitungan manual dikirim ke kantor pusat untuk mendapatkan pencairan dana. Terkadang hasil dari keputusan ini terlalu kaku. Metode yang digunakan untuk memecahkan masalah ini adalah metode RAD dan dalam setiap tahap disesuaikan sesuai berdasarkan algoritma fuzzy Tsukamoto. Dari studi ini, sebuah aplikasi dari sistem pendukung keputusan diperoleh yang dapat mendukung pengambilan keputusan untuk meningkatkan insentif yang lebih tepat dalam pertimbangan manusia. Kata kunci: metode tsukamoto; peningkatan insentif, perancangan sistem pendukung keputusan

Rationality Evaluation on the Fire Compartment of Large-Scale Public Buildings

2012 ◽  
Vol 193-194 ◽  
pp. 1029-1032
Author(s):  
Jia Wang ◽  
Yuan Ren ◽  
Xiao Hong Yin
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Fire Hazard ◽  
Public Buildings ◽  
Fuzzy Algorithm ◽  
Hazard Degree

Nowadays, fire compartments of large-scale public buildings are generally large. This paper analyses and calculates the fire hazard degree of each fire compartment in buildings by Gustav’s method and Fuzzy Algorithm, which can provide a basis for decision-making in reasonable fire compartment.

Exploring destination image through online reviews: an augmented mining model using latent Dirichlet allocation combined with probabilistic hesitant fuzzy algorithm

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuyan Luo ◽  
Tao Tong ◽  
Xiaoxu Zhang ◽  
Zheng Yang ◽  
Ling Li
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Small Sample ◽  
Destination Image ◽  
Tourism Destination ◽  
Fuzzy Algorithm ◽  
Content Type ◽  
Dirichlet Allocation

PurposeIn the era of information overload, the density of tourism information and the increasingly sophisticated information needs of consumers have created information confusion for tourists and scenic-area managers. The study aims to help scenic-area managers determine the strengths and weaknesses in the development process of scenic areas and to solve the practical problem of tourists' difficulty in quickly and accurately obtaining the destination image of a scenic area and finding a scenic area that meets their needs.Design/methodology/approachThe study uses a variety of machine learning methods, namely, the latent Dirichlet allocation (LDA) theme extraction model, term frequency-inverse document frequency (TF-IDF) weighting method and sentiment analysis. This work also incorporates probabilistic hesitant fuzzy algorithm (PHFA) in multi-attribute decision-making to form an enhanced tourism destination image mining and analysis model based on visitor expression information. The model is intended to help managers and visitors identify the strengths and weaknesses in the development of scenic areas. Jiuzhaigou is used as an example for empirical analysis.FindingsIn the study, a complete model for the mining analysis of tourism destination image was constructed, and 24,222 online reviews on Jiuzhaigou, China were analyzed in text. The results revealed a total of 10 attributes and 100 attribute elements. From the identified attributes, three negative attributes were identified, namely, crowdedness, tourism cost and accommodation environment. The study provides suggestions for tourists to select attractions and offers recommendations and improvement measures for Jiuzhaigou in terms of crowd control and post-disaster reconstruction.Originality/valuePrevious research in this area has used small sample data for qualitative analysis. Thus, the current study fills this gap in the literature by proposing a machine learning method that incorporates PHFA through the combination of the ideas of management and multi-attribute decision theory. In addition, the study considers visitors' emotions and thematic preferences from the perspective of their expressed information, based on which the tourism destination image is analyzed. Optimization strategies are provided to help managers of scenic spots in their decision-making.

Discarding optimality: Throwing out the baby with the bathwater?

2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Reward Rate ◽  
Perceptual Decision ◽  
Standard Observer ◽  
Rate Maximization ◽  
Reward Rate Maximization ◽  
Descriptive Standard ◽  
Observer Model

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.

LPCD framework: Analytical tool or psychological model?

2018 ◽  
Vol 41 ◽  
Author(s):  
David Danks
Keyword(s):  
Decision Making ◽  
Analytical Tool ◽  
Mathematical Framework ◽  
Bayesian Decision ◽  
Perceptual Decision Making ◽  
Psychological Model ◽  
Psychological Reality ◽  
Psychological Theories ◽  
Target Article ◽  
Bayesian Decision Making

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.

Elaborating the role of reflection and individual differences in the study of folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
Kevin Arceneaux
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Processes ◽  
Evolutionary History ◽  
Behavioral Responses ◽  
History Of ◽  
Economic Beliefs

AbstractIntuitions guide decision-making, and looking to the evolutionary history of humans illuminates why some behavioral responses are more intuitive than others. Yet a place remains for cognitive processes to second-guess intuitive responses – that is, to be reflective – and individual differences abound in automatic, intuitive processing as well.

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