International Journal of Industrial Engineering & Production Research
Iran University of Science & Technology
Sat, Mar 1, 2025
[Archive]
1- MSc Graduate, Department of Industrial Engineering, Faculty of Industrial and MechanicalEngineering, Qazvin branch, Islamic Azad University, Qazvin, Iran
2- PhD Candidate, Department of Industrial Engineering, Faculty of Industrial and MechanicalEngineering, Qazvin branch, Islamic Azad University, Qazvin, Iran
3- Associate Professor, Department of Industrial Engineering, Faculty of Industrial andMechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran ,alalinezhad@gmail.com
2- PhD Candidate, Department of Industrial Engineering, Faculty of Industrial and MechanicalEngineering, Qazvin branch, Islamic Azad University, Qazvin, Iran
3- Associate Professor, Department of Industrial Engineering, Faculty of Industrial andMechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran ,
Abstract: (261 Views)
Today, data mining and machine learning are recognized as tools for extracting knowledge from large
datasets with diverse characteristics. With the increasing volume and complexity of information in various
fields, decision-making has become more challenging for managers and decision-making units. Data
Envelopment Analysis (DEA) is a tool that aids managers in measuring the efficiency of the units under
their supervision. Another challenge for managers involves selecting and ranking options based on specific
criteria. In such cases, the selection of an appropriate multi-criteria decision-making (MCDM) technique
is crucial. With the spread of COVID-19 and the significant financial, economic, and human losses it caused,
data mining has once again played a role in improving outcomes, predicting trends, and reducing these
losses by identifying patterns in the data. This paper aims to assess and predict the efficiency of countries
in preventing and treating COVID-19 by combining DEA and MCDM models with machine learning models.
By evaluating decision-making units and utilizing available data, decision-makers are better equipped to
make effective decisions in this area. Computational results are presented in detail and discussed in depth.
datasets with diverse characteristics. With the increasing volume and complexity of information in various
fields, decision-making has become more challenging for managers and decision-making units. Data
Envelopment Analysis (DEA) is a tool that aids managers in measuring the efficiency of the units under
their supervision. Another challenge for managers involves selecting and ranking options based on specific
criteria. In such cases, the selection of an appropriate multi-criteria decision-making (MCDM) technique
is crucial. With the spread of COVID-19 and the significant financial, economic, and human losses it caused,
data mining has once again played a role in improving outcomes, predicting trends, and reducing these
losses by identifying patterns in the data. This paper aims to assess and predict the efficiency of countries
in preventing and treating COVID-19 by combining DEA and MCDM models with machine learning models.
By evaluating decision-making units and utilizing available data, decision-makers are better equipped to
make effective decisions in this area. Computational results are presented in detail and discussed in depth.
Keywords: Data Mining, Machine Learning, Data Envelopment Analysis, Multi-Criteria DecisionMaking, COVID-19.
Type of Study: Research |
Subject:
Application of Computer in I.E
Received: 2024/08/20 | Accepted: 2025/01/18
Received: 2024/08/20 | Accepted: 2025/01/18
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