## 1 Introduction
In contrast, others focus on cost minimization [22; 23; 26; 28; 29; 30; 31; 32] without accounting for limited financial resources, which is especially important for developing countries. In [33], the \(\mathit{ENS}\) indicator was minimized while considering the constraints of limited financial resources available for investment. The analyzed test networks assumed that consumption was exclusively residential. Strategies for \(\mathit{ENS}\) improvement were defined, and their selection was carried out to achieve the most significant possible reduction in \(\mathit{ENS}\) within the given financial constraints. Studies [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32] focus on optimizing reliability indicators, whereas other research has examined reliability from different perspectives, which are particularly applicable in advanced systems. These perspectives include the optimization of energy trading under uncertainty [34] and a resilience-oriented strategy for coordinating maintenance and reconfiguration to support post-failure recovery in integrated energy systems [35]. In [36], a coordinated model for multi-energy ship microgrids (MESMs) has been developed. This model introduces load-shedding and energy curtailment schemes to improve the reliability of the MESM. In [37], a P2P outage insurance mechanism has been proposed, facilitating effective risk sharing and fair cost allocation, indirectly enhancing system reliability. Additionally, many methodologies are tailored to developed power systems with advanced regulatory frameworks, leaving a gap in methodologies applicable to developing countries, where semi-urban and rural networks require substantial infrastructure upgrades before deploying advanced technologies on a larger scale. In [5; 12], the importance of tailoring strategies and investments to regional conditions for improving network reliability is emphasized. Serbia is a developing country with a semi-urban and rural distribution network that requires significant improvements in infrastructure, equipment, and reliability before advanced grid technologies can be implemented. It also lacks a regulatory framework for compensating consumers for unsupplied electrical energy. Motivated by these factors, this study proposes a methodology for enhancing network reliability, particularly applicable to distribution networks in developing countries. Building on previous research [33], this study expands the approach by proposing a probabilistic investment optimization methodology that accounts for financial constraints while improving reliability indicators in semi-urban and rural distribution networks serving diverse consumer types. The main objective of this paper is to propose a methodology for enhancing reliability performance in semi-urban and rural distribution networks using Monte Carlo simulation (MCS) to account for uncertainties in input data, ensuring the selection of optimal investment options. While MCS has been widely used in reliability studies due to its ability to capture stochastic uncertainties [4; 14; 16], previous studies have primarily focused on reliability optimization without explicitly considering budget constraints. In contrast, this study incorporates a budget constraint and integrates MCS with Mixed-Integer Linear Programming (MILP) to obtain a more realistic solution and ensure cost-effective reliability improvements. In this study, in comparison to [33], the reliability assessment includes not only the _ENS_ indicator but also the analysis of _SAIDI_, thereby ensuring the practical application of the methodology, as _SAIDI_ is a key indicator within the regulatory framework of most countries. Furthermore, a cost indicator has been introduced as the third minimized indicator compared to [33]. It represents an economic measure associated with supply interruptions, designed for practical use in developing countries. The uncertainties of reliability indices and consumer load were incorporated into the analysis using MCS.