Reverse Logistics Network Path Planning Optimization Strategy Based on Greedy Algorithm

Significant inefficiencies and high operational costs in reverse logistics route planning are exacerbated by pervasive uncertainties in demand volume and its spatial distribution. While forecasting is recognized as crucial, existing approaches often do not adequately address the coupled spatiotemporal nature of reverse logistics demand or do not proactively integrate these forecasts into vehicle deployment strategies prior to detailed routing. This study confronts these challenges by proposing an optimized reverse logistics network path planning strategy that integrates advanced data-driven demand forecasting with proactive vehicle deployment. Specifically, we first develop a four-stage spatiotemporal forecasting method, combining a Gate Recurrent Unit model for temporal volume prediction with gravity constraints for spatial distribution across logistics zones. Subsequently, two greedy algorithm-inspired vehicle deployment strategies are introduced to translate these forecasts into optimal initial vehicle positioning, aiming to maximize demand satisfaction and load utilization before detailed route planning. These deployments then inform a mixed-integer multi-objective route planning model, solved using NSGA-II. Applied to waste home appliance collection in Haidian District, Beijing, our approach demonstrates that proactive vehicle deployment driven by forecast significantly reduces transportation costs and improves operational efficiency compared to conventional methods. This work underscores the critical value of integrating granular spatiotemporal demand intelligence into the strategic phase of reverse logistics planning to mitigate uncertainty.