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Towards the Integration of Power-Indexed Formulations in Multi-Architecture Connected Facility Location Problems for the Optimal Design of Hybrid Fiber-Wireless Access Networks

机译:针对混合光纤无线接入网优化设计的多架构连接设备选址中功率索引配方的集成

摘要

Urban access networks are the external part of worldwide networks that make telecommunication services accessible to end users and represent a critical part of the infrastructures of modern cities. An important recent trend in urban access networks is the integration of fiber and wireless networks, leading to so-called fiber-wireless (Fi-Wi) networks. Fi-Wi networks get the best of both technologies, namely the high capacity offered by optical fiber networks and the mobility and ubiquity offered by wireless networks. The optimal design of fiber and wireless networks has been separately extensively studied. However, there is still a lack of mathematical models and algorithms for the integrated design problem. In this work, we propose a new Power-Indexed optimization model for the 3-architecture Connected Facility Location Problem arising in the design of urban telecommunication access networks. The new model includes additional power-indexed variables and constraints to represent the signal-to-interference formulas expressing wireless signal coverage. To solve the problem, which can prove very hard even for a state-of-the art optimization solver, we propose a new heuristic that combines a probabilistic variable fixing procedure, guided by (tight) linear relaxations, with an MIP heuristic, corresponding to an exact very large neighborhood search. Computational experiments on realistic instances show that our heuristic can find solutions of much higher quality than a state-of-the-art solver.
机译:城市接入网是全球网络的外部部分,使最终用户可以访问电信服务,并且代表了现代城市基础设施的关键部分。城市接入网络的近期重要趋势是光纤和无线网络的集成,从而导致了所谓的光纤无线(Fi-Wi)网络。 Fi-Wi网络充分利用了这两种技术,即光纤网络提供的高容量以及无线网络提供的移动性和普遍性。光纤和无线网络的最佳设计已分别进行了广泛的研究。但是,仍然缺少用于集成设计问题的数学模型和算法。在这项工作中,我们针对城市电信接入网设计中出现的3架构连接设施位置问题提出了一种新的Power-Indexed优化模型。新模型包括额外的功率索引变量和约束,以表示表达无线信号覆盖范围的信号干扰公式。为了解决即使对于最先进的优化求解器来说也很难证明的问题,我们提出了一种新的启发式方法,该方法结合了由(紧)线性松弛指导的概率变量固定过程与MIP启发式方法相结合精确的非常大的邻域搜索。在逼真的实例上进行的计算实验表明,我们的启发式方法可以找到质量比最先进的求解器高得多的解决方案。

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