Enhancing Decision Fusion for Wastewater Treatment System Selection Using Monte Carlo Simulation and Gray Analytic Hierarchy Process

Abstract

This research presents an innovative data fusion model that utilizes Monte Carlo simulations (MC) and the Gray Analytic Hierarchy Process (G-AHP) to address the complexity and uncertainty in decision-making processes, particularly in selecting sustainable wastewater treatment systems. The study critiques and extends the Dempster–Shafer and Yager’s theories by incorporating a novel MC algorithm that mitigates the computational challenges of large numbers of experts and sensors. The model demonstrates superior performance in synthesizing diverse expert opinions and evidence, ensuring comprehensive and probabilistically informed decision-making under uncertainty. The results show that the combined MC algorithm produces satisfactory results, and thus, offers wide applicability in decision-making contexts. To determine its effectiveness, an extensive empirical study was conducted to identify an appropriate wastewater treatment system for the busy city of Tehran, incorporating the insights and perspectives of respected experts in the field. The selection was based on three technical, economic, and environmental–social criteria. Due to the large dimensions of each of the defined criteria, sub-criteria were also defined to achieve better results for each of the criteria. The in-depth analysis conducted revealed that enhanced aeration activated sludge (EAAS) emerged as the best choice for Tehran’s most urgent needs among various competitors, with a remarkable priority rating of 34.48%. Next, the Gray Analytic Hierarchy Process (G-AHP) was used to determine the most important sub-criterion, based on which resistance to hydraulic shock is most important in the enhanced aeration activated sludge system. Due to its versatility in different fields and industries, this method is a powerful tool for managers to optimize system efficiency and identify defects and risks and eventually to minimize costs.