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Applied Research of Improved Hybrid Genetic Algorithm in Multiple Constraints Location - Routing Problem

机译：改进混合遗传算法在多个约束位置 - 路由问题中的应用研究

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The location routing problem (LRP), which simultaneously tackles both facility location and the vehicle routing decisions to minimize the total system cost, is of great importance in designing an integrated logistic distribution network. In this paper a simulated annealing algorithm (SA) based hybrid genetic algorithm was developed to solve the LRP with capacity constraints (CLRP) on depots and routes. The proposed hybrid genetic algorithm modified the population generation method, genetic operators and recombination strategy and realized the combination of the local searching ability of SA and global searching ability of GA. To evaluate the performance of the proposed approach, we conducted an experimental study and compared its results with other heuristics on a set of well-known Barreto Benchmark instances. The experimental results verified the feasibility and effectiveness of our approach.
机译：同时解决设施位置和车辆路径决策的位置路由问题（LRP），以最大限度地减少总系统成本，这在设计集成的逻辑分配网络方面具有重要意义。在本文中，开发了一种基于模拟的退火算法（SA）的混合遗传算法，以解决容量约束（CLRP）在仓库和路线上的LRP。所提出的混合遗传算法改性了人口生成方法，遗传运营商和重组策略，并实现了遗传信息的局部搜索能力的组合和GA的全球搜索能力。为了评估所提出的方法的性能，我们进行了一个实验研究，并将其结果与其他具有众所周知的巴雷托基准实例的结果进行了比较。实验结果验证了我们方法的可行性和有效性。

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