## 3 Investigation of Resilience Investment Strategies
### Repair Management  
Repair management is a short-term resilience-enhancement strategy that involves optimizing repair prioritization and workforce allocation. This study examines the following three distinct repair management approaches:  
**Case 6.**: Tripling the number of repair crews compared to the base case.
**Case 7.**: Assuming an unlimited number of repair crews are available. To realize the scenario, the number of repair crews increased by 3000 times.
**Case 8.**: Not considering the criticality of the loads during restoration.  
Figure 9 compares the EOD and NDLH metrics for Cases 6 and 7 against the base case, demonstrating that both metrics follow a similar trend across cases. The NDLH value is lower than that of EOD, consistent with the reasoning discussed earlier. The figure illustrates a substantial reduction in EOD, from 89.27 hr/incident-event in the base case to 39.38 hr/incident-event in Case 6. Further increasing the repair crew capacity without limit in Case 7 reduces the EOD even more, down to 19.01 hr/incident-event. These results indicate that while increasing repair crew size does improve the system performance, the improvement has a limit, unlike the effects observed with infrastructure hardening.  
Figure 10 presents the percentage reduction in EOD relative to that of the base case for both critical and non-critical loads under Cases 6 and 7. In Case 6, the EOD for critical loads drops by 42.61%, while non-critical loads present a reduction of 56.25%. Case 7 achieves even greater improvements, with reductions of 54.64% and 79.37% for critical and non-critical loads, respectively. These results suggest that increasing repair crew size has a more significant impact on enhancing the resilience of non-critical loads. However, this interpretation can be misleading when taken in isolation. The measure of EOD in the base case is already significantly lower for critical than for non-critical loads. As previously discussed, the benefit of adding repair resources diminishes after a certain point, particularly for loads already exhibiting relatively lower outage durations. Hence, although  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **EOD (hr/int-lp)** & **NDLH (hr/event-lp)** \\ \hline
**Base case** & 89.27 & 86.19 \\ \hline
**Case 4** & 87.39 & 83.92 \\ \hline
**Case 5** & 79.24 & 74.45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of EOD and NDLH for the base case, Case 4, and Case 5.  
Figure 9: EOD and NDLH for cases with repair management.  
critical loads appear to benefit less in relative terms, the absolute improvement across both load types remains comparable.  
Table 3 presents a comparison of the EOD and NDLH metrics for Case 8 with those of the base case. The findings indicate that when the critical loads are prioritized during the restoration process, there is an overall improvement in system performance. This occurs because assigning the highest priority to critical loads has disrupted the optimality of the overall restoration sequence. An additional examination of the EOD values for critical and non-critical loads reveals that the EOD for critical loads increases significantly by 58.8%, whereas the EOD for non-critical loads decreases by only 5.5%. This indicates that the deterioration in the performance for critical loads far outweighs the modest gains observed for non-critical loads.