A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment

• Autorzy: Del Gallo Mateo , Ciarapica Filippo Emanuele , Mazzuto Giovanni , Bevilacqua Maurizio

Identyfikatory

DOI

10.24425/mper.2023.147204

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

data mining; association rules; optimization model; production scheduling; job-shop scheduling; flow shop scheduling

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2023
• Tom: Vol. 14, No. 4
• Strony: 56--70
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr.

Twórcy

autor

Del Gallo Mateo

• Università Politecnica delle Marche, Department of Industrial Engineering and Mathematical Science, Italy – Ancona Via Brecce Bianche 12, 60131

autor

Ciarapica Filippo Emanuele

• Università Politecnica delle Marche, Department of Industrial Engineering and Mathematical Science, Italy

autor

Mazzuto Giovanni

• Università Politecnica delle Marche, Department of Industrial Engineering and Mathematical Science, Italy

autor

Bevilacqua Maurizio

• Università Politecnica delle Marche, Department of Industrial Engineering and Mathematical Science, Italy

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