Oktay KarabagOnder BulutAyhan Ozgur ToyMehmet Murat Fadiloglu2025-10-0620240951-832010.1016/j.ress.2023.109914http://dx.doi.org/10.1016/j.ress.2023.109914https://gcris.yasar.edu.tr/handle/123456789/7590With rapid advances in technology many systems are becoming more complex including ever-increasing numbers of components that are prone to failure. In most cases it may not be feasible from a technical or economic standpoint to dedicate a sensor for each individual component to gauge its wear and tear. To make sure that these systems that may require large capitals are economically maintained one should provide maintenance in a way that responds to captured sensor observations. This gives rise to conditionbased maintenance in partially observable multi -component systems. In this study we propose a novel methodology to manage maintenance interventions as well as spare part quantity decisions for such systems. Our methodology is based on reducing the state space of the multi -component system and optimizing the resulting reduced -state Markov decision process via a linear programming approach. This methodology is highly scalable and capable of solving large problems that cannot be approached with the previously existing solution procedures.EnglishCondition-based maintenance, Spare part quantity, Markov decision process, Linear programming, Stochastic degradation, Partially observable systemsOPTIMIZATION, POLICYArticle