Szczegóły publikacji
Opis bibliograficzny
Control-oriented modelling and quantitative evaluation of maintenance strategies in data-constrained urban public transport fleets / Wasihun KENO, Janusz SZPYTKO // W: MMAR 2026 [Dokument elektroniczny] : 30th international conference on Methods and Models in Automation and Robotics : 18-21 August 2026, Międzyzdroje, Poland : technical papers : on line proceedings. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2026. — ( International Conference on Methods and Models in Automation and Robotics ; ISSN 2835-2815 ). — USB ISBN:979-8-3195-1920-7. — Print on Demand(PoD) ISBN:979-8-3195-1922-1. — e-ISBN: 979-8-3195-1921-4. — S. 363-368. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 367-368, Abstr. — Publikacja dostępna online od: 2026-09-02
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 169811 |
|---|---|
| Data dodania do BaDAP | 2026-10-08 |
| Tekst źródłowy | URL |
| DOI | 10.1109/MMAR70562.2026.11667856 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | International Conference on Methods and Models in Automation and Robotics 2026 |
| Czasopismo/seria | International Conference on Methods and Models in Automation and Robotics |
Abstract
Urban public transport fleets operate under time-varying demand, limited maintenance capacity, and incomplete condition information. This paper studies fleet maintenance under partial observation using a discrete time state space model of stochastic deterioration. Each vehicle is represented by a latent deterioration state that evolves with infrastructure severity and unit level heterogeneity, while noisy and intermittent measurements are incorporated through a recursive state estimation rule. Three policies are compared under identical workshop constraints, namely, corrective maintenance, periodic preventive maintenance, and condition-based maintenance triggered by an estimated deterioration threshold that varies with system load. A Monte Carlo simulation over a 365 day horizon evaluates mean fleet availability, service level, and lifecycle cost. In the simulated scenarios, the condition-based policy gives the best overall performance, with availability gains of up to 12.34 percentage points and lifecycle cost reductions of about 9 percent relative to periodic maintenance in severe deterioration and long repair settings. The advantage declines as observation intervals increase and grows as infrastructure severity and repair duration increase. These findings apply to the simulated setting and show that state informed maintenance can improve fleet performance when observations are sparse and workshop capacity is limited.
