Abstract
Water scarcity and allocation disputes have emerged as major challenges in increasingly urbanizing smart cities, where increasing population density, outdated infrastructure, high water losses, and unequal geographic distribution frequently result in shortages despite adequate overall supply. Traditional techniques, such as linear programming and agent-based modeling, have produced helpful insights, but they are still restricted in capturing varied stakeholder behaviors, assuring equilibrium stability in competitive contexts, and providing spatially adaptable solutions. To address these shortcomings, this study applies the concept of the Nash Equilibrium (NE) model within Game theory (GT) to model strategic interactions among households, industries, utilities, and regulators, each with distinct payoff functions. Once equilibrium is achieved, no stakeholder can unilaterally improve its outcome, thereby guaranteeing fairness and stability. Building on this theoretical foundation, the model integrates Optimized Multi-Objective Particle Swarm Optimization (OMOPSO) to efficiently explore Pareto-optimal trade-offs between economic, social, and environmental objectives, while Geographic Information Systems (GIS) incorporate spatial constraints to deliver geographically realistic allocation strategies. Experimental validation demonstrates that the proposed model consistently outperforms existing approaches within the framework of Multi-Objective Evolutionary Algorithms (MOEAs) in terms of convergence stability and computational efficiency. Beyond algorithmic performance, the findings highlight practical applications for tariff design, consumer incentive programs, infrastructure investment, and water-use restrictions. This study increases stateof-the-art urban water management by integrating GT, evolutionary optimization, and spatial analysis, while also providing policymakers with a strong and fair decision-support framework for sustainable resource allocation.
Keywords: Game Theory, GIS, Nash Equilibrium, OMOPSO, Smart City, Water Management.