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October  2015, 11(4): 1375-1391. doi: 10.3934/jimo.2015.11.1375

## Optimal double-resource assignment for a distributed multistate network

 1 Department of Industrial Management, Tungnan University, New Taipei City, 222, Taiwan

Received  March 2014 Revised  October 2014 Published  March 2015

A distributed multistate network is a multistate network with the flows entering from multiple source nodes and exiting by multiple sink nodes. A multistate network is a network with its nodes and edges having multiple states (capacities) or failures. Such networks are different from the ones solved by the traditional methods in two aspects: the number of source/sink nodes is more than one, and the source nodes are also sink nodes. The optimal double-resource assignment problem for a distributed multistate network (ODRADMN) is to solve the optimal capacity assignment for nodes and edges in the network such that the total capacity requirement of the network is minimized while keeping the network still alive. Traditionally, multi-objective optimization methods are employed to solve such kind of problems. This paper proposes an elegant single-objective optimization method to solve the double-resource optimization problem in terms of network reliability. Several numerical examples are demonstrated to explain the proposed method.
Citation: Shin-Guang Chen. Optimal double-resource assignment for a distributed multistate network. Journal of Industrial & Management Optimization, 2015, 11 (4) : 1375-1391. doi: 10.3934/jimo.2015.11.1375
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##### References:
 [1] Yi-Kuei Lin, Cheng-Ta Yeh. Reliability optimization of component assignment problem for a multistate network in terms of minimal cuts. Journal of Industrial & Management Optimization, 2011, 7 (1) : 211-227. doi: 10.3934/jimo.2011.7.211 [2] Cheng-Ta Yeh, Yi-Kuei Lin. Component allocation cost minimization for a multistate computer network subject to a reliability threshold using tabu search. Journal of Industrial & Management Optimization, 2016, 12 (1) : 141-167. doi: 10.3934/jimo.2016.12.141 [3] Bailey Kacsmar, Douglas R. Stinson. A network reliability approach to the analysis of combinatorial repairable threshold schemes. Advances in Mathematics of Communications, 2019, 13 (4) : 601-612. doi: 10.3934/amc.2019037 [4] Bara Kim. Stability of a retrial queueing network with different classes of customers and restricted resource pooling. Journal of Industrial & Management Optimization, 2011, 7 (3) : 753-765. doi: 10.3934/jimo.2011.7.753 [5] I-Lin Wang, Shiou-Jie Lin. A network simplex algorithm for solving the minimum distribution cost problem. Journal of Industrial & Management Optimization, 2009, 5 (4) : 929-950. doi: 10.3934/jimo.2009.5.929 [6] King Hann Lim, Hong Hui Tan, Hendra G. Harno. Approximate greatest descent in neural network optimization. Numerical Algebra, Control & Optimization, 2018, 8 (3) : 327-336. doi: 10.3934/naco.2018021 [7] Li Gang. An optimization detection algorithm for complex intrusion interference signal in mobile wireless network. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1371-1384. doi: 10.3934/dcdss.2019094 [8] Jiangtao Mo, Liqun Qi, Zengxin Wei. A network simplex algorithm for simple manufacturing network model. Journal of Industrial & Management Optimization, 2005, 1 (2) : 251-273. doi: 10.3934/jimo.2005.1.251 [9] Fengqiu Liu, Xiaoping Xue. Subgradient-based neural network for nonconvex optimization problems in support vector machines with indefinite kernels. Journal of Industrial & Management Optimization, 2016, 12 (1) : 285-301. doi: 10.3934/jimo.2016.12.285 [10] Qiong Liu, Ahmad Reza Rezaei, Kuan Yew Wong, Mohammad Mahdi Azami. Integrated modeling and optimization of material flow and financial flow of supply chain network considering financial ratios. Numerical Algebra, Control & Optimization, 2019, 9 (2) : 113-132. doi: 10.3934/naco.2019009 [11] Yang Chen, Xiaoguang Xu, Yong Wang. Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 887-900. doi: 10.3934/dcdss.2019059 [12] Konstantin Avrachenkov, Giovanni Neglia, Vikas Vikram Singh. Network formation games with teams. Journal of Dynamics & Games, 2016, 3 (4) : 303-318. doi: 10.3934/jdg.2016016 [13] Joanna Tyrcha, John Hertz. Network inference with hidden units. Mathematical Biosciences & Engineering, 2014, 11 (1) : 149-165. doi: 10.3934/mbe.2014.11.149 [14] T. S. Evans, A. D. K. Plato. Network rewiring models. Networks & Heterogeneous Media, 2008, 3 (2) : 221-238. doi: 10.3934/nhm.2008.3.221 [15] David J. Aldous. A stochastic complex network model. Electronic Research Announcements, 2003, 9: 152-161. [16] Pradeep Dubey, Rahul Garg, Bernard De Meyer. Competing for customers in a social network. Journal of Dynamics & Games, 2014, 1 (3) : 377-409. doi: 10.3934/jdg.2014.1.377 [17] Suzana Antunović, Tonči Kokan, Tanja Vojković, Damir Vukičević. Exponential generalised network descriptors. Advances in Mathematics of Communications, 2019, 13 (3) : 405-420. doi: 10.3934/amc.2019026 [18] Ngoc Minh Trang Vu, Laurent Lefèvre. Finite rank distributed control for the resistive diffusion equation using damping assignment. Evolution Equations & Control Theory, 2015, 4 (2) : 205-220. doi: 10.3934/eect.2015.4.205 [19] Jianfeng Feng, Mariya Shcherbina, Brunello Tirozzi. Stability of the dynamics of an asymmetric neural network. Communications on Pure & Applied Analysis, 2009, 8 (2) : 655-671. doi: 10.3934/cpaa.2009.8.655 [20] Zari Dzalilov, Iradj Ouveysi, Alexander Rubinov. An extended lifetime measure for telecommunication network. Journal of Industrial & Management Optimization, 2008, 4 (2) : 329-337. doi: 10.3934/jimo.2008.4.329

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