进化巨环方法用于带容量约束的车辆路径问题：基于节点移位编码与机器学习修复启发式
Evolutionary Giant Tour for CVRP using NSE and ML Heuristic]{Evolutionary Giant Tour approach for CVRP using Node Shift Encoding and Machine Learning repair heuristic
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中文总结 AI 辅助
本文提出一种将进化过程与路线构建分离的框架，采用节点移位编码并引入无监督机器学习修复启发式，在CVRP基准测试中显著提升解质量与可行性。
中文摘要 AI 辅助
带容量约束的车辆路径问题（CVRP）仍是物流研究中的核心问题，因为在严格的容量和车队限制下进行路线优化直接影响运营绩效。遗传算法被广泛用于解决该问题，但其有效性在很大程度上取决于解编码的选择以及在搜索过程中保持可行性的机制。本文提出一个顺序框架，将进化过程与路线构建分离。我们将节点移位编码（NSE）适配到CVRP的巨环设置中，并将其与路径表示和双染色体进行基准比较。我们进一步提出一种无监督的机器学习修复启发式，在无约束的线性分割后恢复车队可行性，并将该流程与文献中采用的车队约束分割进行比较。在标准基准实例上的计算测试表明，NSE在解质量和可行性方面始终优于其他编码。修复启发式在约72%的情况下恢复了可行性，且线性分割-机器学习启发式流程比约束分割更稳健，提供了更低的成本和更高的可行性率。
英文摘要
The Capacitated Vehicle Routing Problem (CVRP) remains a central concern in logistics research, as route optimisation under rigid capacity and fleet restrictions directly affects operational performance. Genetic Algorithms are widely used for this problem, yet their effectiveness depends heavily on the choice of solution encoding and on the mechanisms used to preserve feasibility during the search. This paper proposes a sequential framework that separates the evolutionary process from route construction. We adapt Node Shift Encoding (NSE) to the CVRP giant-tour setting and benchmark it against Path Representation and Double Chromosome. We further propose an unsupervised machine-learning repair heuristic that restores fleet feasibility after an unconstrained linear split, and compare this pipeline against fleet-constrained split taken from the literature. Computational tests on standard benchmark instances show that NSE consistently outperforms the other encodings in solution quality and feasibility. The repair heuristic restores feasibility in approximately 72\% of cases, and the Linear-Split-ML-Heuristic pipeline proves more robust than the constrained split, delivering lower costs and higher feasibility rates.
发表机构
- University M’hamed Bougara of Boumerdes（布米尔达斯穆哈迈德布加拉大学）
机构由 AI 辅助整理，请以论文原文为准。
