论文标题
可靠设施位置问题的混合进化算法
A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem
论文作者
论文摘要
可靠的设施位置问题（RFLP）是运营研究的重要研究主题，在现代供应链和物流的决策和管理中起着至关重要的作用。通过解决RFLP，决策者可以在设施中断或失败的风险下获得可靠的位置决策。在本文中，我们为RFLP提出了一种新型模型。我们没有像在现有作品中那样将固定数量的设施分配给每个客户，而是将分配的设施的数量设置为我们提出的模型中的独立变量，这使我们的模型更接近现实生活中的情况，但更难通过传统方法来解决。为了处理它，我们提出了一种混合进化算法EAMLS，它结合了令人难忘的局部搜索（MLS）方法和进化算法（EA）。此外，提出了一种称为L3值的新型度量，以帮助分析该算法的收敛速度并检查进化过程。实验结果表明，与CPLEX求解器和遗传算法（GA）相比，我们的EAML的有效性和卓越性能在大规模问题上。
The reliable facility location problem (RFLP) is an important research topic of operational research and plays a vital role in the decision-making and management of modern supply chain and logistics. Through solving RFLP, the decision-maker can obtain reliable location decisions under the risk of facilities' disruptions or failures. In this paper, we propose a novel model for the RFLP. Instead of assuming allocating a fixed number of facilities to each customer as in the existing works, we set the number of allocated facilities as an independent variable in our proposed model, which makes our model closer to the scenarios in real life but more difficult to be solved by traditional methods. To handle it, we propose EAMLS, a hybrid evolutionary algorithm, which combines a memorable local search (MLS) method and an evolutionary algorithm (EA). Additionally, a novel metric called l3-value is proposed to assist the analysis of the algorithm's convergence speed and exam the process of evolution. The experimental results show the effectiveness and superior performance of our EAMLS, compared to a CPLEX solver and a Genetic Algorithm (GA), on large-scale problems.
