Application of Fuzzy-AHP Combined with Particle Swarm Optimization (PSO) for Determining Prioritization and Strategies in Debt Collection

Regional public drinking water companies (Perumdam) face persistent challenges in managing customer arrears, which directly affect financial stability, operational performance, and service sustainability. This study applies a hybrid approach by combining the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) and Particle Swarm Optimization (PSO) to determine prioritization and strategies in debt collection. Fuzzy-AHP is employed to handle uncertainty in qualitative assessments and to generate priority weights for decision criteria, while PSO is used to optimize the resulting collection strategies. The dataset includes variables such as arrears amount, payment delays, frequency of payments, and service quality factors. Experimental results show that the proposed Fuzzy-AHP–PSO method provides accurate strategy recommendations with high ranking consistency, as validated through accuracy tests and Spearman’s Rho correlation analysis. Therefore, this approach can serve as an effective decision-support solution for regional public drinking water companies (Perumdam) in optimizing debt collection strategies and improving overall receivables management.