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We present an optimization problem for the maintenance of a multi-component system designed by Thales. This system is subject to random deteriorations and failures of its components, during the missions for which it is required, leading to the evolution of its state and an unavailability penalty in case of failure. The challenge is then to define an optimal policy for the use and maintenance of the system, in order to guarantee the good progress of the missions, while minimizing its management costs. It is a question of determining a compromise between intervening too early, generating unnecessary maintenance costs, and intervening too late, leading to the failure of the system and to paying penalties and more expensive operations.One of the specificities of this work is to consider a sequential decision making on the system. Then, it is about differentiating the maintenance operations according to the state of each of its components. The main idea of this work is then to define a mathematical model for the evolution of the system via the formalism of a Markovian Decision Process (MDP). Thus, the objective is to solve the associated optimization problem, i.e. to determine for each decision date and each state of the system, the action that minimizes the sum of the costs generated over the whole horizon. This is called a policy. We define several preventive and corrective reference policies and compare their performances in terms of costs and failure statistics, by Monte-Carlo simulations. This illustrates the interest of grouping the maintenances during the workshop visits, and of considering the operating times of the components for decision making, in order to reduce both the costs and the failure rate.The model specified for this industrial problem gives rise to a non-standard optimization problem in the MDP framework, because the system state space is continuous and the transition kernel is not analytically explicit, but only simulatable. In order to be able to search for an optimal policy over a finite set ...

We present an optimization problem for the maintenance of a multi-component system designed by Thales. This system is subject to random deteriorations and failures of its components, during the missions for which it is required, leading to the evolution of its state and an unavailability penalty in case of failure. The challenge is then to define an optimal policy for the use and maintenance of the system, in order to guarantee the good progress of the missions, while minimizing its management costs. It is a question of determining a compromise between intervening too early, generating unnecessary maintenance costs, and intervening too late, leading to the failure of the system and to paying penalties and more expensive operations.One of the specificities of this work is to consider a sequential decision making on the system. Then, it is about differentiating the maintenance operations according to the state of each of its components. The main idea of this work is then to define a mathematical model for the evolution of the system via the formalism of a Markovian Decision Process (MDP). Thus, the objective is to solve the associated optimization problem, i.e. to determine for each decision date and each state of the system, the action that minimizes the sum of the costs generated over the whole horizon. This is called a policy. We define several preventive and corrective reference policies and compare their performances in terms of costs and failure statistics, by Monte-Carlo simulations. This illustrates the interest of grouping the maintenances during the workshop visits, and of considering the operating times of the components for decision making, in order to reduce both the costs and the failure rate.The model specified for this industrial problem gives rise to a non-standard optimization problem in the MDP framework, because the system state space is continuous and the transition kernel is not analytically explicit, but only simulatable. In order to be able to search for an optimal policy over a finite set ...

We present an optimization problem for the maintenance of a multi-component system designed by Thales. This system is subject to random deteriorations and failures of its components, during the missions for which it is required, leading to the evolution of its state and an unavailability penalty in case of failure. The challenge is then to define an optimal policy for the use and maintenance of the system, in order to guarantee the good progress of the missions, while minimizing its management costs. It is a question of determining a compromise between intervening too early, generating unnecessary maintenance costs, and intervening too late, leading to the failure of the system and to paying penalties and more expensive operations.One of the specificities of this work is to consider a sequential decision making on the system. Then, it is about differentiating the maintenance operations according to the state of each of its components. The main idea of this work is then to define a mathematical model for the evolution of the system via the formalism of a Markovian Decision Process (MDP). Thus, the objective is to solve the associated optimization problem, i.e. to determine for each decision date and each state of the system, the action that minimizes the sum of the costs generated over the whole horizon. This is called a policy. We define several preventive and corrective reference policies and compare their performances in terms of costs and failure statistics, by Monte-Carlo simulations. This illustrates the interest of grouping the maintenances during the workshop visits, and of considering the operating times of the components for decision making, in order to reduce both the costs and the failure rate.The model specified for this industrial problem gives rise to a non-standard optimization problem in the MDP framework, because the system state space is continuous and the transition kernel is not analytically explicit, but only simulatable. In order to be able to search for an optimal policy over a finite set ...

International audience ; We present an optimization problem for the maintenance of a multi-componentsystem subject to random deteriorations or failures of its components, resulting in the evolutionof the overall system's state. The system can be required to perform missions with deterministicoccurrences and durations. Our long term objective is to set up an optimized maintenance policyto ensure the smooth running of missions while minimizing maintenance costs. The main ideaof this work is to propose a mathematical model for the evolution of the system by using theformalism of a Markov Decision Processes (MDP). Through Monte Carlo simulations, we comparethe performances of several reference policies. ; Nous présentons un problème d'optimisation pour la maintenance d'un système multi-composants sujet à des détériorations ou défaillances aléatoires de ses composants, en-traînant l'évolution de l'état général du système. Le système peut être requis pour effectuer des missions de fréquences et durées déterministes. Notre objectif à long terme est de mettre en place une politique de maintenance optimisée afin de garantir le bon déroulement des missions tout en minimisant les coûts de maintenance. L'idée principale de ce travail est de proposer un modèle mathématique pour l'évolution du système en utilisant le formalisme d'un Processus Marko-vien Décisionnel (MDP). Par simulations de Monte Carlo, nous comparons les performances de plusieurs politiques de référence.

International audience ; We present an optimization problem for the maintenance of a multi-componentsystem subject to random deteriorations or failures of its components, resulting in the evolutionof the overall system's state. The system can be required to perform missions with deterministicoccurrences and durations. Our long term objective is to set up an optimized maintenance policyto ensure the smooth running of missions while minimizing maintenance costs. The main ideaof this work is to propose a mathematical model for the evolution of the system by using theformalism of a Markov Decision Processes (MDP). Through Monte Carlo simulations, we comparethe performances of several reference policies. ; Nous présentons un problème d'optimisation pour la maintenance d'un système multi-composants sujet à des détériorations ou défaillances aléatoires de ses composants, en-traînant l'évolution de l'état général du système. Le système peut être requis pour effectuer des missions de fréquences et durées déterministes. Notre objectif à long terme est de mettre en place une politique de maintenance optimisée afin de garantir le bon déroulement des missions tout en minimisant les coûts de maintenance. L'idée principale de ce travail est de proposer un modèle mathématique pour l'évolution du système en utilisant le formalisme d'un Processus Marko-vien Décisionnel (MDP). Par simulations de Monte Carlo, nous comparons les performances de plusieurs politiques de référence.