Application of machine learning to predict complex nested solutions in hierarchical programming
Subject Areas : Educational
Mahdi Bahrami
1
,
Yaqob alavimatin
2
,
Soliman Iranzadeh
3
1 - PhD student in industrial management majoring in operations research
2 - Assistant Professor, Department of Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran
3 - Associate Professor, Department of Industrial Management, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
Keywords: Machine learning, predictive solutions, planning, hierarchy,
Abstract :
Nowadays, due to the need to reduce costs and increase productivity, we are always witnessing the emergence of new methods in the field of production and operation management, hierarchical production planning is also considered as one of these methods, which has been the subject of considerable research in the past few decades. has been done., therefore, the present research is looking for the application of machine learning to predict complex nested solutions in hierarchical planning. Data collection, this research is descriptive-analytical and library studies were used to collect information. Descriptive researches are used to investigate the existing conditions in order to know more in order to help the decision-making process. The statistical population of the current research is the Mobarake Steel Company of Isfahan. Considering that the research data is few. Data extraction is done from existing documents. As a result, the studied sample is data related to production, cost and financial performance in the years 2015-2019. Sampling in this research is purposeful. Library and field methods were used to collect information in this research. Library studies were used as a foundation to formulate the theoretical framework of the research, and the field method was also used to obtain information about Mobarake Steel Company.
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