Facility location problems have been a major research area of interest in the last several decades. In particular, uncapacitated location problems (ULP) have enormous applications. Variations of ULP often appear, especially as large-scale subproblems in more complex combinatorial optimization problems. Although many researchers have studied different versions of ULP (e.g., uncapacitated facility location problem (UCFLP) and p-Median problem), most of these authors have considered small to moderately sized problems. In this paper, we address the ULP and provide a fast adaptive meta-heuristic for large-scale problems. The approach is based on critical event memory tabu search. For the diversification component of the algorithm, we have chosen a procedure based on a sequencing problem commonly used for traveling salesman-type problems. The efficacy of this approach is evaluated across a diverse range of benchmark problems sourced from the Internet, with a comprehensive comparison against four prominent algorithms in the literature. The proposed adaptive critical event tabu search (ACETS) demonstrates remarkable effectiveness for large-scale problems. The algorithm successfully solved all problems optimally within a short computing time. Notably, ACETS discovered three best new solutions for benchmark problems, specifically for Asymmetric 500A-1, Asymmetric 750A-1, and Symmetric 750B-4, underscoring its innovative and robust nature.

翻译：设施选址问题在过去几十年中一直是重要的研究领域。特别是无容量限制选址问题（ULP）具有广泛的应用。ULP的变体经常出现，尤其是作为更复杂组合优化问题中的大规模子问题。尽管许多研究者已研究过ULP的不同版本（例如无容量限制设施选址问题（UCFLP）和p-中值问题），但大多数研究者考虑的是中小规模问题。本文针对ULP提出了一种适用于大规模问题的快速自适应元启发式算法。该方法基于关键事件记忆禁忌搜索。在算法的多样化组件中，我们选择了基于排序问题的程序，该程序常用于旅行商类问题。通过在互联网来源的多样化基准问题上评估该方法的效能，并与文献中四种著名算法进行全面比较，所提出的自适应关键事件禁忌搜索（ACETS）在大规模问题上展现出显著的有效性。该算法在较短计算时间内成功实现了所有问题的最优求解。值得注意的是，ACETS为基准问题发现了三个新的最佳解，具体针对Asymmetric 500A-1、Asymmetric 750A-1和Symmetric 750B-4实例，这突显了其创新性和鲁棒性。