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Resource definition and allocation for a multi-asset portfolio with heterogeneous degradation

机译：具有异构降级的多资产产品组合的资源定义和分配

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When making long-term plans for their asset portfolios, decision-makers have to define a priori a maintenance budget that is to be shared among the several assets and managed throughout the planning period. During the planning period, the a priori budget is then allocated by managers to different operation and maintenance interventions ensuring the overall performance of the system. Because asset degradation is stochastic, a considerable amount of uncertainty is associated with this problem. Hence, to define a robust budget, it is essential to account for several degradation scenarios pertaining to the individual condition of each asset. This paper presents a novel mathematical formulation to tackle this problem in a heterogeneous multiasset portfolio. The proposed mathematical model was formulated as a mixed-integer programming two-stage stochastic optimization model with mean-variance constraints to minimize the number of scenarios with an insufficient budget. A Gamma process was used to model the condition of each individual asset while taking into consideration different technological features and operating conditions. We compared the solutions obtained with our model to alternative practices in a set of generated instances covering different types of multi-asset portfolios. This comparison allowed us to explore the value of modeling uncertainty and how it affects the generated solutions. The proposed approach led to gains in performance of up to 50% depending on the level of uncertainty. Furthermore, the model was validated using real-world data from a utility company working with portfolios of power transformers. The results obtained showed that the company could reduce costs by as much as 40%. Further conclusions showed that the cost-saving potential was higher in asset portfolios in worse condition and that defining a priori operation and maintenance interventions led to worse results. Finally, the results showcased how different decision-maker risk-levels affect the value of taking uncertainty into account.
机译：在为其资产投资组合制定长期计划时，决策者必须定义先验的维护预算，该预算将在若干资产之间分享，并在整个规划期间管理。在规划期间，然后通过管理员分配先验预算，以确保系统的整体性能的不同操作和维护干预。因为资产劣化是随机的，所以有很大的不确定性与这个问题有关。因此，要定义强大的预算，必须考虑与每个资产的各个条件有关的若干劣化情景。本文介绍了一种新的数学制定，可以在异构Multiagset产品组合中解决这个问题。所提出的数学模型作为混合整数编程两级随机优化模型，其平均方差约束，以最大限度地减少预算不足的场景数量。伽玛工艺用于在考虑不同的技术特征和操作条件的同时建模每个个人资产的状况。我们将通过模型获得的解决方案与涵盖不同类型的多资产投资组合的一组生成的实例中的替代实践进行了比较。此比较使我们探讨了建模不确定性以及它如何影响所生成的解决方案的值。根据不确定性的水平，拟议的方法导致性能高达50％。此外，使用来自公用事业公司的实际数据与电力变压器组合一起使用的实际数据进行验证。得到的结果表明，该公司可以将成本降低到40％。进一步的结论表明，资产投资组合中的成本节省潜力在更糟糕的情况下，定义先验操作和维护干预导致更糟糕的结果。最后，结果表明，不同的决策者风险水平如何影响不确定性的价值。

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