Vehicle routing problem is a widely researched combinatorial optimization problem. We developed a hybrid of three strategies—a modified ant system, a sweep algorithm, and a path relinking—for solving a capacitated vehicle routing optimization problem, a vehicle routing problem with a capacity constraint. A sweep algorithm was used to generate initial solutions and assign customers to vehicles, followed by a modified ant system to generate new generations of good solutions. Path relinking was used for building a better solution (candidate) from a pair of guiding and initial solutions. Finally, a local search method—swap, insert, reverse and greedy search operations—was used to prevent solutions from getting trapped in a local minimum. Performance of the proposed algorithm was evaluated on three datasets from CVRPLIB. Our proposed method was at least competitive to state-of-the-art algorithms in terms of the total route lengths. It even surpassed the best-known solution in the P-n55-k8 instance. Our findings can lower the transportation cost by reducing the travelling distance and efficiently utilizing the vehicle capacity.

中文翻译：

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具有扫描算法和路径重新链接的混合改进蚂蚁系统用于解决容量车辆路径问题


车辆路径问题是一个广泛研究的组合优化问题。我们开发了三种策略的混合——改进的蚂蚁系统、扫描算法和路径重新链接——用于解决有容量的车辆路径优化问题、有容量约束的车辆路径问题。使用扫描算法生成初始解决方案并将客户分配给车辆，然后使用修改后的蚂蚁系统生成新一代的良好解决方案。路径重新链接用于从一对引导解决方案和初始解决方案构建更好的解决方案（候选方案）。最后，使用局部搜索方法（交换、插入、反向和贪婪搜索操作）来防止解决方案陷入局部最小值。在 CVRPLIB 的三个数据集上评估了所提出算法的性能。我们提出的方法至少在总路线长度方面与最先进的算法具有竞争力。它甚至超越了 P-n55-k8 实例中最著名的解决方案。我们的研究结果可以通过减少行驶距离和有效利用车辆容量来降低运输成本。