A self-learning framework combining association rules and mathematical models to solve production scheduling programs
| Autor: | Mateo Del Gallo, Sara Antomarioni, Giovanni Mazzuto, Giulio Marcucci, Filippo Emanuele Ciarapica |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Production and Manufacturing Research: An Open Access Journal, Vol 12, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 21693277 2169-3277 |
| DOI: | 10.1080/21693277.2024.2332285 |
| Popis: | ABSTRACTData-driven production scheduling and control systems are essential for manufacturing organisations to quickly adjust to the demand for a wide range of bespoke products, often within short lead times. This paper presents a self-learning framework that combines association rules and optimization techniques to create data-driven production scheduling. A new approach to predicting interruptions in the production process through association rules was implemented, using a mathematical model to sequence production activities in real or near real-time. The framework was tested in a case study of a ceramics manufacturer, updating confidence values by comparing planned values to actual values recorded during production control. It also sets a production corrective factor based on confidence value and success rate to avoid product shortages. The results were generated in just 1.25 seconds, resulting in a makespan reduction of 9% and 6% compared to two heuristics based on First-In-First-Out and Short Processing Time strategies. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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