Predictive Scheduling with Markov Chains and ARIMA Models
Artykuł w czasopiśmie
MNiSW
100
Lista 2021
| Status: | |
| Autorzy: | Sobaszek Łukasz, Gola Arkadiusz, Kozłowski Edward |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2020 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 17 |
| Wolumen/Tom: | 10 |
| Numer artykułu: | 6121 |
| Strony: | 1 - 19 |
| Impact Factor: | 2,679 |
| Web of Science® Times Cited: | 10 |
| Scopus® Cytowania: | 11 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | The project/research was financed from the Lublin University of Technology Project—Regional Initiative of Excellence from the funds of the Ministry of Science and Higher Education on the basis of a contract No. 030/RID/2018/19. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 3 września 2020 |
| Abstrakty: | angielski |
| Production scheduling is attracting considerable scientific interest. Effective scheduling of production jobs is a critical element of smooth organization of the work in an enterprise and, therefore, a key issue in production. The investigations focus on improving job scheduling effectiveness and methodology. Due to simplifying assumptions, most of the current solutions are not fit for industrial applications. Disruptions are inherent elements of the production process and yet, for reasons of simplicity, they tend to be rarely considered in the current scheduling models. This work presents the framework of a predictive job scheduling technique for application in the job-shop environment under the machine failure constraint. The prediction methods implemented in our work examine the nature of the machine failure uncertainty factor. The first section of this paper presents robust scheduling of production processes and reviews current solutions in the field of technological machine failure analysis. Next, elements of the Markov processes theory and ARIMA (auto-regressive integrated moving average) models are introduced to describe the parameters of machine failures. The effectiveness of our solutions is verified against real production data. The data derived from the strategic machine failure prediction model, employed at the preliminary stage, serve to develop the robust schedules using selected dispatching rules. The key stage of the verification process conce |
