A Data-Driven Three-Layer Algorithm for Split Delivery Vehicle Routing Problem with 3D Container Loa
Xijun Li (Huawei Noah's Ark Lab); Mingxuan Yuan (Huawei Noah's Ark Lab); Di Chen (Huawei Noah's Ark Lab); Jianguo Yao (Shanghai Jiao Tong University); Jia Zeng (Huawei Noah's Ark Lab)
Split Delivery Vehicle Routing Problem with 3D Loading Constraints (3L-SDVRP) can be seen as the most important problem in large-scale manufacturing logistics. The goal is to devise a strategy consisting of three NP-hard planning components: vehicle routing, cargo splitting and container loading, which shall be jointly optimized for cost savings. The problem is an enhanced variant of the classical logistics problem 3L-CVRP, and its complexity leaps beyond current studies of solvability. Our solution employs a novel data-driven three-layer search algorithm (DTSA), which we designed to improve both the efficiency and effectiveness of traditional meta-heuristic approaches, through learning from data and from simulation.
A detailed experimental evaluation on real data shows our algorithm is versatile in solving this practical complex constrained multi-objective optimization problem, and our framework may be of general interest. DTSA performs much better than the state-of-the-art algorithms both in efficiency and optimization performance. Our algorithm has been deployed in the UAT (User Acceptance Test) environment; conservative estimates suggest that the full usage of our algorithm would save millions of dollars in logistics costs per year, besides savings due to automation and more efficient routing.