@ARTICLE{SAHAR_Habbadi_Optimization_2023,
 author={SAHAR, Habbadi and HERROU, Brahim and SEKKAT, Souhail},
 volume={vol. 14},
 number={No 3},
 journal={Management and Production Engineering Review},
 howpublished={online},
 year={2023},
 publisher={Production Engineering Committee of the Polish Academy of Sciences, Polish Association for Production Management},
 abstract={The Job Shop scheduling problem is widely used in industry and has been the subject of study by several researchers with the aim of optimizing work sequences. This case study provides an overview of genetic algorithms, which have great potential for solving this type of combinatorial problem. The method will be applied manually during this study to understand the procedure and process of executing programs based on genetic algorithms. This problem requires strong decision analysis throughout the process due to the numerous choices and allocations of jobs to machines at specific times, in a specific order, and over a given duration. This operation is carried out at the operational level, and research must find an intelligent method to identify the best and most optimal combination. This article presents genetic algorithms in detail to explain their usage and to understand the compilation method of an intelligent program based on genetic algorithms. By the end of the article, the genetic algorithm method will have proven its performance in the search for the optimal solution to achieve the most optimal job sequence scenario.},
 title={Optimization of Job Shop Scheduling Problem by Genetic Algorithms: Case Study},
 URL={http://journals.pan.pl/Content/128807/PDF-MASTER/art04_corr.pdf},
 doi={10.24425/mper.2023.147189},
 keywords={Optimization, Metaheuristics, scheduling, Job Shop Scheduling problem, genetic algorithms, Simulation},
}