In this paper, we address the Capacitated Facility Location Problem (CFLP) in which the assignment of facilities to customers must ensure enough facility capacity and all the customers must be served. We propose both sequential and parallel Relaxation Adaptive Memory Programming approaches for the CFLP, combining a Lagrangean subgradient search with an improvement method to explore primal-dual relationships to create advanced memory structures that integrate information from both primal and dual solution spaces. Computational experiments of the effectiveness of this approach are presented and discussed.

中文翻译：

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用于有能力设施位置问题的 RAMP 算法

在本文中，我们解决了容量化设施位置问题 (CFLP)，其中向客户分配设施必须确保足够的设施容量，并且必须为所有客户提供服务。我们为 CFLP 提出了顺序和并行松弛自适应内存编程方法，将拉格朗日次梯度搜索与改进方法相结合，探索原始对偶关系，以创建集成原始和对偶解决方案空间信息的高级内存结构。介绍并讨论了这种方法有效性的计算实验。