This paper addresses a multiattribute vehicle routing problem, the rich vehicle routing problem, with time constraints, heterogeneous fleet, multiple depots, multiple routes, and incompatibilities of goods. Four different approaches are presented and applied to 15 real datasets. They are based on two meta‐heuristics, ant colony optimization (ACO) and genetic algorithm (GA), that are applied in their standard formulation and combined as hybrid meta‐heuristics to solve the problem. As such ACO‐GA is a hybrid meta‐heuristic using ACO as main approach and GA as local search. GA‐ACO is a memetic algorithm using GA as main approach and ACO as local search. The results regarding quality and computation time are compared with two commercial tools currently used to solve the problem. Considering the number of customers served, one of the tools and the ACO‐GA approach outperforms the others. Considering the cost, ACO, GA, and GA‐ACO provide better results. Regarding computation time, GA and GA‐ACO have been found the most competitive among the benchmark.

中文翻译：

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复杂的多属性车辆路径问题的混合生物启发式元启发法设计

本文解决了多属性车辆路径问题，丰富的车辆路径问题，时间约束，车队异构，多个仓库，多条路线以及商品不兼容问题。提出了四种不同的方法并将其应用于15个真实数据集。它们基于两种元启发式方法，即蚁群优化（ACO）和遗传算法（GA），将其应用于其标准公式中，并作为混合元启发式方法加以组合以解决该问题。因此，ACO-GA是一种混合元启发式方法，使用ACO作为主要方法，而GA作为本地搜索。GA‐ACO是一种模因算法，使用GA作为主要方法，将ACO作为局部搜索。将有关质量和计算时间的结果与当前用于解决该问题的两种商业工具进行比较。考虑到服务的客户数量，其中一种工具和ACO-GA方法优于其他工具。考虑到成本，ACO，GA和GA‐ACO可提供更好的结果。关于计算时间，GA和GA‐ACO被认为是基准测试中最具竞争力的。