Fleet-level prognostics and health management (PHM) requires decisions that extend beyond diagnosis, remaining useful life estimation, and maintenance timing. Mission commitment, aircraft dispatch, maintenance, and spare-parts support consume the same finite sustainment capacity but operate at different temporal scales. This study investigates whether explicitly separating a mission-horizon commitment decision from day-to-day sustainment execution improves fleet-level decision quality, and under which operating regimes such a hierarchy is valuable. We develop a mission-horizon hierarchical reinforcement learning (MH-HRL) framework in which a learned weekly policy selects a portfolio of missions from a seven-day forecast and specifies a coarse sustainment posture, while a fixed daily executor implements aircraft assignment, maintenance prioritization, and replenishment decisions. Fleet evolution is represented by a next-event discrete-event simulator with stochastic mission arrivals, degradation/failure processes, repairs, and logistics lead times. Five decision policies—reactive rule-based control, rolling-horizon planning, flat PPO, a hierarchy without the learned posture signal, and the full MH-HRL policy—are evaluated over nine mission-load/support-capacity regimes using 10 paired common-random-number seeds (450 formal runs). The hierarchical policies consistently improve the composite mission-readiness-cost utility, with the full policy outperforming the rule baseline in all nine regimes after Holm correction. The benefit is not obtained by maximizing mission acceptance: the hierarchical policy accepts fewer missions but achieves higher accepted-mission success and fleet availability while substantially reducing failure-driven sustainment cost. Ablation further shows that approximately 97% of the Smart vs. Rule improvement is attributable to the weekly daily temporal decomposition rather than the additional posture signal. These results support that the principal value of hierarchy in the studied fleet-PHM setting is long-horizon commitment control, and its marginal value increases with mission pressure.
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.
Carmine Giardino, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 175 citations· ⚡19
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
It is found that roles of MVPs in startups were not fully aware by entrepreneurs, and entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP).
Anh Nguyen-Duc, P. Abrahamsson· International Conference on...· 93 citations· ⚡9
It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.
Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al.· International Conference on...· 62 citations· ⚡6
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 14, 2026
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.