E3S Web Conf.
Volume 243, 2021The 5th International Conference on Power, Energy and Mechanical Engineering (ICPEME 2021)
|Number of page(s)||5|
|Section||Mechanical Engineering and Industrial Automation|
|Published online||11 March 2021|
Improved Genetic Algorithm Integrated with Scheduling Rules for Flexible Job Shop Scheduling Problems
School of Mechanical and Manufacturing Engineering (SMME), National University of Science and Technology (NUST), Islamabad, Pakistan
* Corresponding author:firstname.lastname@example.org
This paper presents optimization of makespan for Flexible Job Shop Scheduling Problems (FJSSP) using an Improved Genetic Algorithm integrated with Rules (IGAR). Machine assignment is done by Genetic Algorithm (GA) and operation selection is done using priority rules. Improvements in GA include a new technique of adaptive probabilities and a new forced mutation technique that positively ensures the generation of new chromosome. The scheduling part also proposed an improved scheduling rule in addition to four standard rules. The algorithm is tested against two well-known benchmark data set and results are compared with various algorithms. Comparison shows that IGAR finds known global optima in most of the cases and produces improved results as compared to other algorithms.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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