This paper deals with the optimization of industrial asset management strategies, whose profitability is characterized by the Net Present Value (NPV) indicator which is assessed by a Monte Carlo simulator. The developed method consists in building a metamodel of this stochastic simulator, allowing to obtain, for a given model input, the NPV probability distribution without running the simulator. The present work is concentrated on the emulation of the quantile function of the stochastic simulator by interpolating well chosen basis functions and metamodeling their coefficients (using the Gaussian process metamodel). This quantile function metamodel is then used to treat a problem of strategy maintenance optimization (four systems installed on different plants), in order to optimize an NPV quantile. Using the Gaussian process framework, an adaptive design method (called quantile function expected improvement) is defined by extending in our case the well-known efficient global optimization algorithm. This allows to obtain an "optimal" solution using a small number of simulator runs.
The long term management of a production asset raises several major issues among which rank the technical management of the plant, its economics and the fleet level perspective one has to adopt. Decision makers are therefore faced with the need to define long term policies (up to the end of asset operation) which take into account multiple criteria including safety (which is paramount) and performance. In this paper we first remind the reader of the EDF three-level methodology for asset management. As introduced in PVP 2003 and PVP 2004, this methodology addresses the component/technical level (how to safely operate daily and invest for the future), the plant level (how to translate technical decisions into plant-wide consequences including economic performance) and the fleet level (how to manage a large number of similar assets). We then focus on the software tool that implements this methodology in order to allow decision makers to define, evaluate and analyze long term plant operation and maintenance policies. Lastly we show how the methodology and the software tool were used on a pilot case study. The technical and economic results obtained at the plant level are described as well as the conclusions one can draw from them in order to help decision makers evaluate and analyze long term asset management strategies.
To ensure a power generation level, the French national electricity supply (EDF) has to manage its producing assets by putting in place adapted preventive maintenance strategies. In this paper, a fleet of identical components is considered, which are spread out all around France (one per power plant site). The components are assumed to have stochastically independent lifetimes but they are made functionally dependent through the sharing of a common stock of spare parts. When available, these spare parts are used for both corrective and preventive replacements, with priority to corrective replacements. When the stock is empty, replacements are delayed until the arrival of new spare parts. These spare parts are expensive and their manufacturing time is long, which makes it necessary to rigorously define their ordering process. The point of the paper is to provide the decision maker with the tools to take the right decision (make or not the overhaul). To do that, two indicators are proposed, which are based on an economic variable called the Net Present Value (NPV). The NPV stands for the difference between the cumulated discounted cash-flows of the purely corrective policy and the one including the overhaul. Piecewise Deterministic Markov Processes (PDMPs) are first considered for the joint modelling of the stochastic evolution of the components, stock and ordering process with and without overhaul. The indicators are next expressed with respect to these PDMPs, which have to be numerically assessed. Instead of using the most classical Monte Carlo (MC) simulations, we here suggest alternate methods based on quasi Monte Carlo simulations, which replace the random uniform numbers of the MC method by deterministic sequences called Low Discrepancy Sequences. The obtained results show a real gain of the quasi Monte Carlo methods in comparison with the MC method. The developed tools can hence help the decision maker to take the right decision.
International audienceTo improve the management of maintenance planning and spare parts ordering of a fleet of components, different investments plans need to be compared. A new investments plan is compared with a reference one through an economic variable called the Net Present Value (NPV). Classically, Monte Carlo simulations are used to assess economic indicators such as the expected NPV and the probability for the NPV to be negative which stands for the probability to regret the performed investments plan. In this document, we propose to use quasi Monte Carlo methods as an alternative to the Monte Carlo (MC) method, which replace the random uniform numbers of the MC method by deterministic sequences with better uniformity properties
The long term management of a production asset raises several major issues, among which rank the technical management of the plant, its economics, and the fleet level perspective one has to adopt. Decision makers are therefore faced with the need to define long term policies (up to the end of asset operation) that take into account multiple criteria including safety (which is paramount) and performance. In this paper we first remind the reader of the EDF three-level methodology for asset management. As introduced in PVP 2003 and PVP 2004, this methodology addresses the component/technical level (how to safely operate daily and invest for the future), the plant level (how to translate technical decisions into plant-wide consequences including economic performance), and the fleet level (how to manage a large number of similar assets). We then focus on the software tool that implements this methodology in order to allow decision makers to define, evaluate, and analyze long term plant operation and maintenance policies. Lastly we show how the methodology and the software tool were used on a pilot case study. The technical and economic results obtained at the plant level are described as well as the conclusions one can draw from them in order to help decision makers evaluate and analyze long term asset management strategies.
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