Oktay KarabağÖnder BulutAyhan Özgür ToyMehmet Murat FadilogluToy, Ayhan ÖzgürKarabağ, OktayBulut, ÖnderFadıloğlu, Mehmet Murat2025-10-062024095183200951-83201879-083610.1016/j.ress.2023.1099142-s2.0-85181763817https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181763817&doi=10.1016%2Fj.ress.2023.109914&partnerID=40&md5=b0b9b77a0cd928301b7a55aca4ae4b9ehttps://gcris.yasar.edu.tr/handle/123456789/8224https://doi.org/10.1016/j.ress.2023.109914With 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 condition-based 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. © 2024 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/openAccessCondition-based Maintenance, Linear Programming, Markov Decision Process, Partially Observable Systems, Spare Part Quantity, Stochastic Degradation, Condition Based Maintenance, Markov Processes, Stochastic Systems, Linear-programming, Markov Decision Processes, Multicomponents Systems, Number Of Components, Optimal Maintenance, Partially Observable Systems, Spare Part Quantity, Spare Parts, Stochastic Degradation, Linear ProgrammingCondition based maintenance, Markov processes, Stochastic systems, Linear-programming, Markov Decision Processes, Multicomponents systems, Number of components, Optimal maintenance, Partially observable systems, Spare part quantity, Spare parts, Stochastic degradation, Linear programmingCondition-Based MaintenanceLinear ProgrammingMarkov Decision ProcessStochastic DegradationPartially Observable SystemsSpare Part QuantityArticle