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.

中文翻译：

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可靠设施定位问题的混合进化算法

可靠设施选址问题（RFLP）是运筹学的一个重要研究课题，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故障的风险下获得可靠的位置决策。在本文中，我们提出了一种新的 RFLP 模型。在我们提出的模型中，我们不是像现有工作那样假设每个客户分配固定数量的设施，而是将分配的设施数量设置为自变量，这使得我们的模型更接近现实生活中的场景，但更难被用传统方法解决。为了处理它，我们提出了 EAMLS，一种混合​​进化算法，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-value 的新度量，以帮助分析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 EAMLS 在大规模问题上的有效性和卓越性能。