Optimizing Waste Facility Locations Using a Flexible Model that Balances Cost, Environmental Impact, and Community Needs

Abstract

This study presents the Dynamic Multi-Objective Uncapacitated Facility Location Problem (DMUFLP) model, a novel and forward-thinking approach designed to enhance facility location decisions in complex and evolving supply chain systems. Unlike traditional models such as the Uncapacitated Facility Location Problem (UFLP), which often prioritize economic efficiency while overlooking environmental and social impacts, the DMUFLP offers a holistic and adaptive framework. It integrates economic, environmental, and social cost dimensions, supports dynamic capacity adjustments, and incorporates stochastic elements to effectively manage uncertainty and fluctuating demand over time. To evaluate the model’s effectiveness, a comprehensive simulation using synthetic data was conducted, benchmarking the DMUFLP against conventional models across various sample sizes. Results consistently demonstrate the DMUFLP’s superior performance in critical metrics, including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Error (MAE), and Area Under the Curve (AUC). Importantly, the model achieves a robust balance between predictive accuracy and model simplicity, offering decision-makers a practical, reliable, and sustainability-focused tool for long-term facility planning. By bridging the gap between economic viability, environmental stewardship, and social responsibility, this study positions the DMUFLP model as a next-generation solution capable of reshaping facility location optimization in real-world applications.