Neural network application for fuzzy multi-criteria decision making problems

Golmohammadi, Davood

doi:10.1016/j.ijpe.2011.01.015

Cited by 70 publications

(34 citation statements)

References 32 publications

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“…For problems with a small number of examples (less than 20) and 2-3 attributes according to the classification methods of calculation can be carried out manually. For the problems with large dimension, these methods are not suitable [5].…”

Section: Methods and Algorithmsmentioning

confidence: 99%

Developing methods and algorithms for a decisionmaking intellectual support in personnel management systems

Даниленко¹

2015

Collection of Selected Papers of the I International Conference on Information Technology and Nanotechnology

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Abstract. This paper proposes methods and algorithms for an intellectual support of decision-making management based on neuro-fuzzy networks. The algorithm for automatic generation of knowledge base is developed in accordance with the criteria of completeness and minimality. Reducing space is made on the basis of expert estimates.Keywords: decision-making; intellectual support; fuzzy logic; neural networks, management system Citation: Danilenko A.N. Developing methods and algorithms for a decisionmaking intellectual support in personnel management systems.

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Section: Methods and Algorithmsmentioning

confidence: 99%

Developing methods and algorithms for a decisionmaking intellectual support in personnel management systems

Даниленко¹

2015

Collection of Selected Papers of the I International Conference on Information Technology and Nanotechnology

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“…Golmohammadi (2011) applied ANN (Artificial Neural Network) method to select the best one among the thirty one suppliers for eight products of a firm in automotive industry. Firstly, an artificial neural network model was designed depending on the managers' decisions for evaluating the supplier's performance then the supplier scores were obtained as a result of the application by redesigning the model by discussing inputs and outputs of the model.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of fuzzy TOPSIS and generalized Choquet integral methods to select the best supplier

Yıldız¹,

Yayla²

2017

10.5267/j.dsl

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“…Usually, the mean square error (MSE) was used [57], where y ij is the network output for example i at processing element j, and d ij is the desired output.…”

Section: Neural Network Evaluation Modelmentioning

confidence: 99%

“…7 Scores of robustness, productivity and sustainability of Wuxi's taxi industry response to variation of the input. During this process, the network learning is disabled so that the weights are no affected [57]. The target input is varied between its maximum and minimum value while all other inputs are fixed, and the outputs of varied target input is computed.…”

Section: Sensitivity Analysismentioning

confidence: 99%

A data-driven quantitative assessment model for taxi industry: the scope of business ecosystem’s health

Zhang

Zhong

Jiang

2017

Eur. Transp. Res. Rev.

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Introduction The taxi industry has boomed over the years, both in street-hail and dispatch market. However, few studies focus on comprehensive perspectives, which make decisions such as implementing regulative or incentive policies difficult. As an economic community working in the value-oriented process, the taxi industry requires a holistic performance evaluation to determine how to adapt strategies to survive and grow. Methods This paper proposed the concept BTaxi Industry Health degree^based on the theory of Business Ecosystem and the evaluation of its health. A four-layer criteria set is developed from aspects of robustness, productivity and sustainability, and weights are determined through Analytic Hierarchy Process. Results A synthetic evaluating model combining Fuzzy Comprehensive Evaluation and Artificial Neural Network is used to maintain the goal of multi-criteria decision-making. With the GPS and Taximeter dataset of taxicabs in the whole taxi industry in Wuxi, the model is applied to empirical studies. Conclusions This paper provides sensibility analysis on not only the company's order volatility, revenue growth and utilization of resources, but also influence on citizen's welfare, energy consumption and environmental pollution, which enables practical regulations and policies within taxi industry.

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Neural network application for fuzzy multi-criteria decision making problems

Cited by 70 publications

References 32 publications

Developing methods and algorithms for a decisionmaking intellectual support in personnel management systems

Developing methods and algorithms for a decisionmaking intellectual support in personnel management systems

Application of fuzzy TOPSIS and generalized Choquet integral methods to select the best supplier

A data-driven quantitative assessment model for taxi industry: the scope of business ecosystem’s health

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