论文标题
相互依存的基础架构的多系统干预优化
Multi-system intervention optimization for interdependent infrastructure
论文作者
论文摘要
现代社会的福祉取决于其基础设施网络的功能。本文介绍了3C概念,一种用于管理基础架构干预措施(例如,维护,翻新等)的综合多系统和多利益相关者优化方法。拟议的方法利用了通过分组(即优化)干预活动来实现的好处。干预优化可通过减少系统中断数量来节省直接干预成本(操作员)和间接无法可用成本(社会)。提出的优化方法被形式化为一个结构化的数学模型,该模型可以说明多个基础设施网络与对多个利益相关者(例如社会和基础设施运营商)的影响之间的相互作用,并且可以容纳不同类型的干预措施,例如维护,去除和升级。使用建议的相互作用矩阵(IM)对基础架构内部和跨基础架构内部和跨基础结构进行了不同类型的相互依存关系。 IM允许整合通常独立计划干预措施的不同基础架构网络的干预措施。此外,引入的3C概念解释了中央干预措施,这是必须在预先建立的时刻发生的情况,在该时刻既不允许延迟也不允许前进。为了证明所提出的方法的适用性,引入了多系统和多功能干预计划的说明性示例。结果表明,运营商和社会成本大大降低。此外,分析中获得的最佳干预计划没有可预测的模式,这表明它是有用的管理决策支持工具。
The wellbeing of modern societies is dependent upon the functioning of their infrastructure networks. This paper introduces the 3C concept, an integrative multi-system and multi-stakeholder optimization approach for managing infrastructure interventions (e.g., maintenance, renovation, etc.). The proposed approach takes advantage of the benefits achieved by grouping (i.e., optimizing) intervention activities. Intervention optimization leads to substantial savings on both direct intervention costs (operator) and indirect unavailability costs (society) by reducing the number of system interruptions. The proposed optimization approach is formalized into a structured mathematical model that can account for the interactions between multiple infrastructure networks and the impact on multiple stakeholders (e.g., society and infrastructure operators), and it can accommodate different types of intervention, such as maintenance, removal, and upgrading. The different types of interdependencies, within and across infrastructures, are modeled using a proposed interaction matrix (IM). The IM allows integrating the interventions of different infrastructure networks whose interventions are normally planned independently. Moreover, the introduced 3C concept accounts for central interventions, which are those that must occur at a pre-established time moment, where neither delay nor advance is permitted. To demonstrate the applicability of the proposed approach, an illustrative example of a multi-system and multi-actor intervention planning is introduced. Results show a substantial reduction in the operator and societal costs. In addition, the optimal intervention program obtained in the analysis shows no predictable patterns, which indicates it is a useful managerial decision support tool.