A Decision Support System for Reverse Logistics Network Design: Integrating Multi-Factorial Forecasting of Solar Panel End-of-Life Assets

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Abstract
The rapid global deployment of solar photovoltaic (PV) technology presents a significant and often overlooked challenge: the effective management of end-of-life (EoL) solar panels. This issue is particularly acute in developing and emerging economies, where established reverse logistics infrastructure is often lacking. A critical limitation in current academic literature is the oversimplified forecasting of EoL waste streams, which fails to account for the dynamic interplay of socio-economic, policy, and environmental variables. To bridge this gap, we propose a novel decision support system (DSS) for the design of a sustainable reverse logistics network. Our system uniquely integrates a hybrid, multi-factorial forecasting model combining a SARIMAX time series approach with a Gradient Boosting Regressor to provide a robust prediction of waste volume. The output of this predictive engine dynamically informs a multi objective, mixed integer linear programming (MILP) model, which optimizes the network design to simultaneously minimize economic costs and environmental impacts. Our findings demonstrate that this integrated framework offers a methodologically more realistic and adaptable approach for strategic planning than existing models, as validated through simulated scenarios. Our study provides an actionable blueprint for policymakers and industry leaders to proactively build a resilient and circular economy for a sustainable energy future.

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