Simulation-Driven Metaheuristic Optimization for Recycling Facility Selection: Enhancing Urban Construction and Demolition Waste Management

Rapid urbanization is driving sharp growth in construction and demolition waste (CDW), making recycling facility selection and transport planning critical for cost-effective and sustainable urban waste management. This paper presents an end-to-end, simulation-driven decision-support framework that jointly optimizes facility selection and operational waste transportation policies under uncertainty, and systematically benchmarks competing solutions using Data Envelopment Analysis (DEA). The proposed approach embeds a metaheuristic optimization engine within a Monte Carlo simulation environment to evaluate facility configurations and dispatch–allocation decisions under stochastic waste generation and operating conditions, using sample-average performance to ensure fair and consistent comparison across scenarios. Results from the Wuhan metropolitan case study show that coordinating dispatch intensity with contracted facility capacity significantly reduces total cost and unmoved waste while stabilizing performance across stochastic realizations; DEA then provides transparent efficiency-frontier ranking across economic, operational, and environmental indicators without requiring pre-specified weights. These findings demonstrate that dispatch–capacity alignment is a dominant lever for robust and sustainable CDW logistics, and that DEA-based benchmarking enhances decision transparency when multiple near-optimal solutions coexist.