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Simultaneous Facility Location and Path Optimization in Static and Dynamic Networks
IEEE Transactions on Control of Network Systems ( IF 4.2 ) Pub Date : 2020-05-19 , DOI: 10.1109/tcns.2020.2995831 Amber Srivastava , Srinivasa M. Salapaka
IEEE Transactions on Control of Network Systems ( IF 4.2 ) Pub Date : 2020-05-19 , DOI: 10.1109/tcns.2020.2995831 Amber Srivastava , Srinivasa M. Salapaka
We present a framework for solving simultaneously the problems of facility location and path optimization in static and dynamic spatial networks. In the static setting, the objective is to determine facility locations and transportation paths from each node to the destination via the network of facilities such that the total cost of commodity transportation is minimized. This is an NP-hard problem. We propose a novel stage-wise viewpoint of the paths which is instrumental in designing the decision variable space in our framework. We use the maximum entropy principle to solve the resulting optimization problem. In the dynamic setting, nodes and destinations are dynamic. We design an appropriate control Lyapunov function to determine the time evolution of facilities and paths such that the transportation cost at each time instant is minimized. Our framework enables quantifying attributes of the facilities and transportation links in terms of the decision variables. Consequently, it becomes possible to incorporate application specific constraints on individual facilities, links, and network topology. We demonstrate the efficacy of our proposed framework through extensive simulations.
中文翻译:
静态和动态网络中同时设施的位置和路径优化
我们提出了一个框架,用于同时解决静态和动态空间网络中设施位置和路径优化的问题。在静态设置中,目标是确定通过设施网络从每个节点到目的地的设施位置和运输路径,以使商品运输的总成本最小化。这是一个NP难题。我们提出了一种新颖的路径阶段观点,这有助于设计框架中的决策变量空间。我们使用最大熵原理来解决由此产生的优化问题。在动态设置中,节点和目的地是动态的。我们设计了一个适当的控制Lyapunov函数来确定设施和路径的时间演变,从而使每个时刻的运输成本最小化。我们的框架可以根据决策变量来量化设施和运输环节的属性。因此,可以将特定于应用程序的约束合并到各个设施,链接和网络拓扑中。我们通过广泛的仿真演示了我们提出的框架的有效性。
更新日期:2020-05-19
中文翻译:
静态和动态网络中同时设施的位置和路径优化
我们提出了一个框架,用于同时解决静态和动态空间网络中设施位置和路径优化的问题。在静态设置中,目标是确定通过设施网络从每个节点到目的地的设施位置和运输路径,以使商品运输的总成本最小化。这是一个NP难题。我们提出了一种新颖的路径阶段观点,这有助于设计框架中的决策变量空间。我们使用最大熵原理来解决由此产生的优化问题。在动态设置中,节点和目的地是动态的。我们设计了一个适当的控制Lyapunov函数来确定设施和路径的时间演变,从而使每个时刻的运输成本最小化。我们的框架可以根据决策变量来量化设施和运输环节的属性。因此,可以将特定于应用程序的约束合并到各个设施,链接和网络拓扑中。我们通过广泛的仿真演示了我们提出的框架的有效性。