## 2 Related Works  
To present the results of the following studies that use GA and multi-criteria optimization methods in different fields, Table 1 summarizes the key aspects of each paper. It includes references to the respective studies, the focus of each work, the applied models, results, and the limitations of the proposed approaches  
[PERSON] et al. [21] present a multi-criterion genetic optimization algorithm for addressing distribution network problems in supply chain management, integrating GA with analytic hierarchy processes (AHPs) [22]. GA generates potential solutions iteratively and evaluates their fitness based on predefined criteria. In the proposed approach, the AHP enhances GA by enabling decision-makers to assign weightings to multiple criteria using pairwise comparisons, facilitating more informed decision-making. Chromosome representation captures demand allocations among customers, warehouses, and manufacturers, with a mechanism to split and optimize orders exceeding supply capacities. Fitness values are calculated using the AHP, considering total cost, delivery time, and capacity utilization,providing a robust multi-criterion evaluation. Operators such as selection, crossover, mutation, and monitoring refine the solution pool iteratively, ensuring convergence towards optimal solutions. Numerical results demonstrate that the combined GA+AHP algorithm is reliable, offering decision-makers greater control and insight into distribution network optimization. This approach improves decision-making by balancing cost, efficiency, and service level priorities effectively. Job scheduling is a key factor influencing the efficiency of complex, heterogeneous distributed systems, such as computational Grids. These environments demand multi-criteria scheduling algorithms to address their diverse operational requirements effectively. [PERSON] et al. (2019) introduce a multi-criteria GA that prioritizes minimizing security risks and power consumption alongside job completion time. While GAs are well-suited for large-scale problems like job scheduling, their computational demands can be prohibitive for real-time applications. To overcome this limitation, we propose an Accelerated Genetic Algorithm (AGA), optimized for faster convergence, and an AGA with Overhead (AGAwO) ([PERSON] et al., 2019), which incorporates the algorithm's computational delay. A simulation-based evaluation compares the AGA and AGAwO with established heuristics, demonstrating their superior performance across multiple criteria. The proposed approach leverages an open queuing network model with geographically distributed clusters, utilizing diverse processor characteristics for better energy efficiency and security. The results confirm the effectiveness of the AGA and AGAwO in achieving balanced and efficient job scheduling in multi-factor computational Grids. In optimizing electrical distribution systems, Pareto front analysis effectively handles multiple conflicting objectives. [PERSON] et al. (2019) focus on reconfiguration to minimize network losses and energy not supplied, employing a GA-based solver to build and update the Pareto front. Key innovations include extended crossover and mutation operators, and ranking solutions using multi-criteria decision-making methods like AHP and TOPSIS ([PERSON] et al., 2019). These enhancements allow automatic decision-making support and identification of preferred solutions on the Pareto front. The approach is validated on reference test networks, revealing effective multi-objective optimization and insightful performance comparisons using geometrical metrics. This framework is extendable to microgrids and scalable networks, offering a comprehensive optimization strategy. [PERSON] et al. (2019) develop a two-stage framework for distributed energy system design combining \(\epsilon\)-constraint optimization and multi-criteria evaluation.