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An intelligent manufacturing system for heat treatment scheduling

This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable to deal with such problems rather than on theoretical developments which are as yet to be applied.

This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Therefore, several intelligent algorithms are developed for this proposal namely, GA, SGA, GACD, AGA, and MGA. These algorithms have been employed to develop efficient intelligent algorithm using Algorithm Portfolio methodology. After that all algorithms have been tested on two types of scheduling benchmarks.

Using the algorithms, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the system was working correctly.

furnace model and heat treatment system model
Furnace model and heat treatment system model

Meet the Principal Investigator(s) for the project

Dr Maysam Abbod
Dr Maysam Abbod - Education Dr Maysam F. Abbod (FIET, CEng, SMIEEE, SFHEA) He received BSc degree in Electrical Engineering fromUniversity of Technology in 1987. PhD in Control Engineering fromUniversity ofSheffield in 1992. From 1993 to 2006 he was with the Department of Automatic Control and Systems Engineering at theUniversity of Sheffield as a research associate and senior research fellow

Related Research Group(s)

machine digital (3)

Digital Manufacturing - Being at the forefront of solutions for building smart machines, we create an operational framework for the digital transformation to Industry 4.0.


AI Social and Digital Innovation - Social, economic and strategic effects of AI and associated technologies. Impact of AI and related technologies on societies, organisations and individuals.

Partnering with confidence

Organisations interested in our research can partner with us with confidence backed by an external and independent benchmark: The Knowledge Exchange Framework. Read more.

Project last modified 20/07/2021