@ARTICLE{DEL_GALLO_Mateo_A_2023,
 author={DEL GALLO, Mateo and CIARAPICA, Filippo Emanuele and MAZZUTO, Giovanni and BEVILACQUA, Maurizio},
 volume={vol. 14},
 number={No 4},
 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={Production problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a selflearning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied.},
 title={A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment},
 URL={http://ochroma.man.poznan.pl/Content/130045/PDF/955_corr.pdf},
 doi={10.24425/mper.2023.147204},
 keywords={data mining, association rules, Optimization model, production scheduling, Job-shopscheduling, Flow-shop scheduling},
}