Activity-led learning approach and group performance analysis using fuzzy rule-based classification model

Purpose – The purpose of this paper is to present a fuzzy-rule-based model to classify spare parts inventories considering multiple criteria for better management of maintenance activities to overcome production down situation. Design/methodology/approach – Fuzzy-rule-based approach for multi-criteria decision making is used to classify the spare parts inventories. Total cost is computed for each group considering suitable inventory policies and compared with other existing models. Findings – Fuzzy-rule-based multi-criteria classification model provides better results as compared to aggregate scoring and traditional ABC classification. This model offers the flexibility for inventory management experts to provide their subjective inputs. Practical implications – The web-based model developed in this paper can be implemented in various industries such as manufacturing, chemical plants, and mining, etc., which deal with large number of spares. This method classifies the spares into three categories A, B and C considering multiple criteria and relationships among those criteria. The framework is flexible enough to add additional criteria and to modify fuzzy-rule-base at any point of time by the decision makers. This model can be easily integrated to any customized Enterprise Resource Planning applications. Originality/value – The value of this paper is in applying Fuzzy-rule-based approach for Multi-criteria Inventory Classification of spare parts. This rule-based approach considering multiple criteria is not very common in classification of spare parts inventories. Total cost comparison is made to compare the performance of proposed model with the traditional classifications and the result shows that proposed fuzzy-rule-based classification approach performs better than the traditional ABC and gives almost the same cost as aggregate scoring model. Hence, this method is valid and adds a new value to spare parts classification for better management decisions.

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DECO 3 RUM: A Differential Evolution learning approach for generating compact Mamdani fuzzy rule-based models

Expert Systems with Applications ◽

10.1016/j.eswa.2017.04.026 ◽

2017 ◽

Vol 83 ◽

pp. 257-272 ◽

Cited By ~ 15

Author(s):

Nikolaos L. Tsakiridis ◽

John B. Theocharis ◽

George C. Zalidis

Keyword(s):

Differential Evolution ◽

Fuzzy Rule ◽

Learning Approach ◽

Rule Based ◽

Evolution Learning

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A Neuro-Fuzzy Rule-Based Classifier Using Important Features and Top Linguistic Features

International Journal of Intelligent Information Technologies ◽

10.4018/ijiit.2016070103 ◽

2016 ◽

Vol 12 (3) ◽

pp. 38-50 ◽

Cited By ~ 7

Author(s):

Saroj Kr. Biswas ◽

Monali Bordoloi ◽

Heisnam Rohen Singh ◽

Biswajit Purkayastha

Keyword(s):

Fuzzy Rule ◽

Fuzzy Classification ◽

Classification Model ◽

Linguistic Features ◽

Fuzzy Classifier ◽

Rule Based ◽

Neuro Fuzzy ◽

Proposed Model ◽

Rule Based Classifier ◽

Rule Based Approach

The efficient feature selection for predictive and accurate classification is highly desirable in many application domains. Most of the attempts to neuro-fuzzy classifier lose information to build interpretable neuro-fuzzy classification model. This paper proposes an interpretable neuro-fuzzy classification model with significant features without loss of knowledge, which is an extension of an existing interpretable neuro-fuzzy classification model. The proposed model is designed based on the consideration of feature importance that is determined by frequency of linguistic features. The rules are then made based on important features. Therefore, the knowledge acquired in network can be comprehended to logical rules using only important features. The proposed model finally performs classification task by rule-based approach. The average accuracy calculated by 10-fold cross validation finds that the proposed model can increase performance of the already proven neuro-fuzzy system for classification tasks.

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