A Model to Optimize the Allocation of Public Administrative Services

Abstract

This paper presents an advanced multi-objective optimization model that integrates the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with fuzzy logic to enhance the allocation of public administrative services. The model effectively balances key objectives, including minimizing client-to-service distances, reducing waiting times, maximizing service coverage, and optimizing resource utilization, while handling uncertainties and conflicting criteria inherent in real-world applications. The model was applied to various case studiesin the Valle de Toluca, demonstrating substantial improvements over traditional methods. Specifically, it achieved a 15% improvement in Pareto front convergence, a 12% increase in service coverage, and a 20% reduction in travel distances for service workers. These results highlight the model’s ability to providemore efficient, equitable, and practical solutions for public service allocation. By improving the operational efficiency and equity of public service distribution, this model offers a powerful tool for decision-makers in public administration. Portions of this work have been previously published, showcasing the model’s effectiveness in optimizing service allocation in real-world contexts. The paper concludes by suggesting future research directions, such as dynamic parameter adjustment and the integration of machine learning to further enhance the model’s capabilities.