Application of Optimization Algorithms in Voter Service Module Allocation

Abstract

Allocation models are essential tools for optimally distributing client requests across multiple services under defined restrictions and objective functions. This study evaluates several heuristics to address an allocation problem involving young individuals reaching voting age. A five-step methodology was implemented: defining variables, executing heuristics, compiling results, evaluating outcomes, and selecting the most effective heuristic. Using experimental data from the Mexican National Electoral Institute (INE), the study focuses on 88,107 individuals aged 17–18 in the 16 municipalities of the Toluca Valley, who can access any of the 10 INE service modules. Six heuristics were analyzed in sequence: genetic algorithm, ant colony optimization, local search, tabu search, simulated annealing, and greedy algorithm. The results indicate that genetic algorithm significantly reduces the processing time when used as the initial heuristic. Furthermore, given the current capacity of the 10 INE modules, serving the entire target population would require nine working days. These findings align with principles of spatial justice and highlight the practical efficiency of heuristic-based solutions in administrative resource allocation. The main contribution of this study is the development and evaluation of a hybrid heuristic framework for allocating INE modules, demonstrating that combining multiple heuristics—with a genetic algorithm as the initial phase—significantly improves solution quality and computational efficiency.