## 1 Introduction
The semi-urban and rural test networks used for algorithm testing and verification included different consumer types. Compared to [33], this work expands by incorporating test networks with nodes that include industrial and commercial consumers in addition to residential ones. This aspect further reinforces the significance of the study, as the results confirm that the position of different consumer types within the network affects the decision-making process when determining the sequence of actions required to maximize reliability improvement. For each analyzed network, predefined strategies and individual options for improving reliability indices were established, as well as the different levels of available investment funds. The strategies defined for each network included (1) installing new components, (2) replacing old components with new ones, and (3) upgrading both components and network functionality. The second strategy, which involves proactive replacement of components with an increased failure rate before they cause an outage, is not addressed in other works, particularly in [11; 12; 13; 14; 17; 20; 21; 22; 23; 26; 28; 29; 30; 31; 32]. An optimization algorithm was developed to address the problem formulated in this study. It is described in the paper, tested, and verified. The key aspects of reliability improvement are listed below, and Table 1 provides a comparison with the relevant literature that examines these aspects. The aspects are categorized as follows:  
* Reliability: (R1) Assessment of the impact of different measures using reliability indicator(s); (R2) Optimization of reliability indicators. * Investments: (I1) Explicit incorporation of financial constraints in the consideration of the limited financial resources available to the DSO for investment; (I2) Minimization of investment in improvement measures, regardless of the DSO's available limited financial resources. * Strategies: (S1) Strategy of installation of new components; (S2) Strategy of replacement of old components with new ones; (S3) Strategy of upgrading existing components. The authors' study extends prior research by presenting the following key contributions:  
* Applying optimization of reliability indicators while considering limited investment funds, simultaneously incorporating strategies S1-S3 into the methodology, and taking into account ID1-ID2. * A distinct contribution of the authors' study is the consideration of I1 and the application of strategy S2 alongside other strategies. * An advancement of this study compared to [33] is: (i) The reliability assessment is based on multiple indicators; (ii) Consideration of different types of consumption, which has proven important for investment decision-making; (iii) Accounting for uncertainties in input data to obtain a more realistic representation, as the calculations do not rely solely on fixed values. * The methodology can be effectively applied in selecting investment options for both lower and higher available budgets. The methodology's effectiveness has been successfully demonstrated for different levels of DSO investment funds. In addition to selecting investment options for each considered funding level, the results demonstrate how each option individually contributes to improving the three observed indicators. This enables the implementation of a sequence  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**References**} & \multicolumn{2}{c}{**Reliability**} & \multicolumn{2}{c}{**Investments**} & \multicolumn{3}{c}{**Strategies**} & \multicolumn{3}{c}{**Input Data**} \\ \cline{2-11}  & **R1** & **R2** & **I1** & **I2** & **S1** & **S2** & **S3** & **ID1** & **ID2** \\ \hline
[11] & ✓ & - & - & - & ✓ & - & - & -  
that prioritizes options with a greater impact on reliability improvement. The proposed methodology provides a structured investment prioritization framework that helps distribution system operators make data-driven decisions on reliability improvements while optimizing their limited funds.