Reliability and Maintenance Data Set for Resilience Optimization

This is a dataset with component reliability data, maintenance data, repaircrew data, routing distances for the following paper submitted to RESS for potential publication.
Ryan O’Neil, Claver Diallo, Abdelhakim Khatab, Nidhal Rezg (2024). Enhancing Critical Network Infrastructure Resilience Through Optimal Post-Disruption Maintenance and Routing Decisions, submitted to Reliability Engineering and System Safety, Elsevier.

This data is for experiment 4 in the paper. This set of experiments considers the network displayed in Figure 18, which includes a known source node s, sink node t, and multiple transshipment nodes. The arc index and capacity (in cubic meters per day, CMD) are indicated on each network arc. The network comprises a total of 31 components/arcs. When all components are functioning, the maximum achievable network flow is 112 CMD. With a demand set at d = 112 CMD, there are S = 43 d-MCs.

本数据集为提交至RESS（《可靠性工程与系统安全》，Reliability Engineering and System Safety，爱思唯尔出版）以待发表的学术论文配套实验数据，涵盖组件可靠性数据、运维数据、维修班组数据与路径距离数据。相关论文信息：Ryan O’Neil、Claver Diallo、Abdelhakim Khatab、Nidhal Rezg（2024），论文题为《通过最优灾后运维与路径决策提升关键网络基础设施韧性》。
本数据集对应论文中的实验4。本系列实验以图18所示的网络为研究对象，该网络包含已知的源节点s、汇节点t以及多个中转节点。每条网络弧段均标注了弧索引与容量（单位为立方米每日，CMD）。该网络总计包含31个组件/弧段。当所有组件均正常运行时，网络可实现的最大流量为112 CMD。当设定需求为d=112 CMD时，共存在S=43个d-最小割集（d-MCs）。