Simultaneous Facility Location and Path Optimization in Static and Dynamic Networks

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.

[1]  R. Varga Geršgorin And His Circles , 2004 .

[2]  Yunpeng Wang,et al.  Percolation transition in dynamical traffic network with evolving critical bottlenecks , 2014, Proceedings of the National Academy of Sciences.

[3]  Zhengyuan Zhou,et al.  Distributed Multi-Depot Routing without Communications , 2014, ArXiv.

[4]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[5]  Timothy Soper,et al.  A shortest path problem on a network with fuzzy arc lengths , 2000, Fuzzy Sets Syst..

[6]  Srinivasa M. Salapaka,et al.  Combined resource allocation and route optimization in multiagent networks: A scalable approach , 2017, 2017 American Control Conference (ACC).

[7]  Shlomo Havlin,et al.  Spreading of localized attacks in spatial multiplex networks , 2017, ArXiv.

[8]  Zhengyuan Zhou,et al.  Least action routing: Identifying the optimal path in a wireless relay network , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[9]  Michael Watson,et al.  Supply Chain Network Design: Applying Optimization and Analytics to the Global Supply Chain , 2012 .

[10]  M. Tribus,et al.  Probability theory: the logic of science , 2003 .

[11]  Roger Wattenhofer,et al.  An Algorithmic Approach to Geographic Routing in Ad Hoc and Sensor Networks , 2008, IEEE/ACM Transactions on Networking.

[12]  Amir Bashan,et al.  Localized attacks on spatially embedded networks with dependencies , 2015, Scientific Reports.

[13]  Rubén Ruiz,et al.  A Fast Algorithm for Finding the Bi-objective Shortest Path in Complicated Networks , 2018, 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD)).

[14]  K. Rose Deterministic annealing for clustering, compression, classification, regression, and related optimization problems , 1998, Proc. IEEE.

[15]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[16]  Eduardo Sontag A Lyapunov-Like Characterization of Asymptotic Controllability , 1983, SIAM Journal on Control and Optimization.

[17]  Sonia Martínez,et al.  Coverage control for mobile sensing networks , 2002, IEEE Transactions on Robotics and Automation.

[18]  Timos Sellis,et al.  A Fast Algorithm for Optimally Finding Partially Disjoint Shortest Paths , 2018, IJCAI.

[19]  Zhengyuan Zhou,et al.  An Efficient Algorithm for a Visibility-Based Surveillance-Evasion Game , 2014 .

[20]  Puneet Sharma,et al.  A Scalable Approach to Combinatorial Library Design for Drug Discovery , 2008, J. Chem. Inf. Model..

[21]  Yunwen Xu,et al.  Aggregation of Graph Models and Markov Chains by Deterministic Annealing , 2014, IEEE Transactions on Automatic Control.

[22]  Meena Mahajan,et al.  The Planar k-means Problem is NP-hard I , 2009 .

[23]  Zhengyuan Zhou,et al.  Smart Greedy Distributed Allocation in Microgrids , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[24]  Yun Lin,et al.  Location-Routing Problem with Simultaneous Home Delivery and Customer's Pickup for City Distribution of Online Shopping Purchases , 2016 .

[25]  Marc Barthelemy,et al.  Morphogenesis of Spatial Networks , 2017 .

[26]  Eduardo Sontag A universal construction of Artstein's theorem on nonlinear stabilization , 1989 .

[27]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[28]  Marc Barthelemy,et al.  Spatial Networks , 2010, Encyclopedia of Social Network Analysis and Mining.

[29]  Srinivasa M. Salapaka,et al.  Maximum Entropy Principle-Based Algorithm for Simultaneous Resource Location and Multihop Routing in Multiagent Networks , 2012, IEEE Transactions on Mobile Computing.

[30]  Andrew V. Goldberg,et al.  Computing the shortest path: A search meets graph theory , 2005, SODA '05.

[31]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[32]  Mayank Baranwal Entropy-based framework for combinatorial optimization problems and enabling the grid of the future , 2018 .

[33]  Puneet Sharma,et al.  Entropy-Based Framework for Dynamic Coverage and Clustering Problems , 2012, IEEE Transactions on Automatic Control.

[34]  Decio R. M. Faria,et al.  A System to improve the management of 5G and IoT Networks by determining the Mobile Position , 2019, Journal of Microwaves, Optoelectronics and Electromagnetic Applications.