A student from the department of production engineering gets Ph.D in decision-making for the problem of batch scheduling multiple products

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A student from the department of production engineering gets Ph.D in decision-making for the problem of batch scheduling multiple products

The graduate student Wathiq Hayawi gets a PhD in Industrial Engineering from the Department of Production Engineering from the University of Technology with a very good grade for his thesis entitled:
(Decision Making for Multi-product Batch Scheduling Problem: Supply Chain)
In the discussion held on Thursday 8/6/2017 at the hall of Dr. Ibrahim Mahmoud Mansour in the department.
The discussion committee consisted of professors: Lamia Mohammed Dawood as President, Assist. Prof. dr. Luma Adnan Hameed, from the Department of Production Engineering , University of Technology,Assist. Prof. Dr. Zuhair Issa Ahmed from Al Albani College, University of Science and Technology, Assist. Prof. Dr. Samir Ali Amin, Department of Machine and Equipment Engineering, University of Technology, and Assist. Prof. Dr. Ali Hussein Hassan, Sumer University, Computer Science. Asist. Prof. dr. Sawsan Sabeeh Abd. Assist. Prof. Dr. Mahmoud Abbas Mahmoud, from the Department of Production Engineering at the University of Technology as supervisors.
The researcher pointed out that the focus of this study on the integration of decision-making within the series of processing company produces several products and using modern methods and methods such as artificial neural networks and genetic algorithm.
The internal processing series was divided into three main sections: Marketing, Production and Procurement. This study was implemented at Wasit Cotton Products Company in the textile factory. It produces five products: "fabric, planner, poplin, dyed and dyed poplin". All these products are made of cotton, Stages to become a final product.
The results obtained showed the accuracy of the application forecasting process, using the absolute average error criterion to measure the accuracy of the test phase prediction for the Elman Method neural networks and for the textile plant products.


Source : Uot Media Date :22/6/2017