Abstract
In this paper, we examine a stochastic two-echelon vehicle routing problem (2e-VRP) using cargo bikes within hyperconnected networks. The focus is on the integration of both delivery and pickup of reusable containers, incorporating transshipment operations, time windows, and stochastic demand constraints. The model also considers the flow consolidation for empty and full containers at the satellites and allows for load splitting. Moreover, this study introduces an innovative gradient-descent-based optimization framework to handle the combinatorial complexity of the proposed model, opening new avenues in stochastic programming. Furthermore, the performance of this novel method is compared against the sample average approximation method, evaluating both solution quality and computational efficiency. Experimental results demonstrate the model’s advanced integration and flexibility, significantly enhancing urban delivery systems and advancing logistics and transportation optimization research.
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