doi: 10.3934/jimo.2020020

Maximizing reliability of the capacity vector for multi-source multi-sink stochastic-flow networks subject to an assignment budget

Computer Science Branch, Mathematics Department, Faculty of Science, Aswan University, Aswan, Egypt

* Corresponding author: M. R. Hassan

Received  May 2019 Revised  August 2019 Published  January 2020

Many real-world networks such as freight, power and long distance transportation networks are represented as multi-source multi-sink stochastic flow network. The objective is to transmit flow successfully between the source and the sink nodes. The reliability of the capacity vector of the assigned components is used an indicator to find the best flow strategy on the network. The Components Assignment Problem (CAP) deals with searching the optimal components to a given network subject to one or more constraints. The CAP in multi-source multi-sink stochastic flow networks with multiple commodities has not yet been discussed, so our paper investigates this scenario to maximize the reliability of the capacity vector subject to an assignment budget. The mathematical formulation of the problem is defined, and a proposed solution based on genetic algorithms is developed consisting of two steps. The first searches the set of components with the minimum cost and the second searches the flow vector of this set of components with maximum reliability. We apply the solution approach to three commonly used examples from the literature with two sets of available components to demonstrate its strong performance.

Citation: M. R. Hassan. Maximizing reliability of the capacity vector for multi-source multi-sink stochastic-flow networks subject to an assignment budget. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2020020
References:
[1]

A. AissouA. Daamouche and M. R. Hassan, Optimal components assignment problem for stochastic-flow networks, Journal of Computer Science, 15 (2019), 108-117.   Google Scholar

[2]

S. G. Chen, An optimal capacity assignment for the robust design problem in capacitated flow networks, Applied Mathematical Modelling, 36 (2012), 5272-5282.  doi: 10.1016/j.apm.2011.12.034.  Google Scholar

[3]

S. G. Chen, Optimal double-resource assignment for the robust design problem in multistate computer networks, Applied Mathematical Modelling, 38 (2014), 263-277.  doi: 10.1016/j.apm.2013.06.020.  Google Scholar

[4]

D. W. Coit and A. E. Smith, Penalty guided genetic search for reliability design optimization, Computers and Industrial Engineering, 30 (1996), 895-904.   Google Scholar

[5]

B. DengizF. Altiparmak and A. E. Smith, Local search genetic algorithm for optimal design of reliable networks, IEEE Transactions on Evolutionary Computation, 10 (1997), 179-188.   Google Scholar

[6]

M. Gen and R. Cheng, Genetic Algorithms and Engineering Optimization, 1$^{st}$ edition, Wiley Series in Engineering, Design, and Automation, 2000. Google Scholar

[7]

M. R. Hassan and H. Abdou, Multi-objective components assignment problem subject to lead-time constraints, Indian Journal of Science and Technology, 11 (2018), 1-9.   Google Scholar

[8]

M. R. Hassan, Solving a component assignment problem for a stochastic-flow network under lead-time constraint, Indian Journal of Science and Technology, 8 (2015), 1-5.   Google Scholar

[9]

M. R. Hassan, Solving flow allocation problems and optimizing system reliability of multisource multisink stochastic flow network, The International Arab Journal of Information Technology (IAJIT), 13 (2016), 477-483.   Google Scholar

[10]

C. C. Hsieh and Y. T. Chen, Reliable and economic resource allocation in an unreliable flow network, Computers and Operations Research, 32 (2005), 613-628.   Google Scholar

[11]

C. C. Hsieh and Y. T. Chen, Simple algorithms for updating multi-resource allocations in an unreliable flow network, Computers and Industrial Engineering, 50 (2006), 120-129.   Google Scholar

[12]

C. C. Hsieh and M. H. Lin, Reliability-oriented multi-resource allocation in a stochastic-flow network, Reliability Engineering and System Safety, 81 (2003), 155-161.   Google Scholar

[13]

Y.-K. Lin and C. T. Yeh, A two-stage approach for a multi-objective component assignment problem for a stochastic-flow network, Engineering Optimization, 45 (2013), 265-285.  doi: 10.1080/0305215X.2012.669381.  Google Scholar

[14]

Y. K. Lin and C. T. Yeh, System reliability maximization for a computer network by finding the optimal two-class allocation subject to budget, Applied Soft Computing, 36 (2015), 578-588.   Google Scholar

[15]

Y. K. Lin and C. T. Yeh, Determining the optimal double-component assignment for a stochastic computer network, Omega, 40 (2012), 120-130.   Google Scholar

[16]

Y. K. Lin and C. T. Yeh, Evaluation of optimal network reliability under components-assignments subject to transmission budget, IEEE Transactions on Reliability, 59 (2010), 539-550.   Google Scholar

[17]

Y. K. Lin and C. T. Yeh, Maximal network reliability with optimal transmission line assignment for stochastic electric power networks via genetic algorithms, Applied Soft Computing, 11 (2011), 2714-2724.   Google Scholar

[18]

Y. K. Lin and C. T. Yeh, Multi-objective optimization for stochastic computer networks using NSGA-Ⅱ and TOPSIS, European Journal of Operational Research, 218 (2012), 735-746.  doi: 10.1016/j.ejor.2011.11.028.  Google Scholar

[19]

Y. K. Lin and C. T. Yeh, Multistate components assignment problem with optimal network reliability subject to assignment budget, Applied Mathematics and Computation, 217 (2011), 10074-10086.  doi: 10.1016/j.amc.2011.05.001.  Google Scholar

[20]

Y. K. Lin and C. T. Yeh, Optimal resource assignment to maximize multistate network reliability for a computer network, Computers and Operations Research, 37 (2010), 2229-2238.  doi: 10.1016/j.cor.2010.03.013.  Google Scholar

[21]

Y. K. Lin and C. T. Yeh, Computer network reliability optimization under double-source assignments subject to transmission budget, Information Sciences, 181 (2011), 582-599.   Google Scholar

[22]

Y. K. LinC. T. Yeh and P. S. Huang, A hybrid ant-tabu algorithm for solving a multistate flow network reliability maximization problem, Applied Soft Computing, 13 (2013), 3529-3543.   Google Scholar

[23]

Q. LiuH. Z. Xiaoxian and Q. Zhao, Genetic algorithm-based study on flow allocation in a multicommodity stochastic-flow network with unreliable nodes, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 8 (2007), 576-581.   Google Scholar

[24]

Q. LiuQ. Z. Zhao and W. K. Zang, Study on multi-objective optimization of flow allocation in a multi-commodity stochastic-flow network with unreliable nodes, Journal of Applied Mathematics Computing (JAMC), 28 (2008), 185-198.  doi: 10.1007/s12190-008-0093-9.  Google Scholar

[25]

M. J. ZuoZ. Tian and H. Z. Huang, An efficient method for reliability evaluation of multistate networks given all minimal path vectors, IIE Transactions, 39 (2007), 811-817.   Google Scholar

show all references

References:
[1]

A. AissouA. Daamouche and M. R. Hassan, Optimal components assignment problem for stochastic-flow networks, Journal of Computer Science, 15 (2019), 108-117.   Google Scholar

[2]

S. G. Chen, An optimal capacity assignment for the robust design problem in capacitated flow networks, Applied Mathematical Modelling, 36 (2012), 5272-5282.  doi: 10.1016/j.apm.2011.12.034.  Google Scholar

[3]

S. G. Chen, Optimal double-resource assignment for the robust design problem in multistate computer networks, Applied Mathematical Modelling, 38 (2014), 263-277.  doi: 10.1016/j.apm.2013.06.020.  Google Scholar

[4]

D. W. Coit and A. E. Smith, Penalty guided genetic search for reliability design optimization, Computers and Industrial Engineering, 30 (1996), 895-904.   Google Scholar

[5]

B. DengizF. Altiparmak and A. E. Smith, Local search genetic algorithm for optimal design of reliable networks, IEEE Transactions on Evolutionary Computation, 10 (1997), 179-188.   Google Scholar

[6]

M. Gen and R. Cheng, Genetic Algorithms and Engineering Optimization, 1$^{st}$ edition, Wiley Series in Engineering, Design, and Automation, 2000. Google Scholar

[7]

M. R. Hassan and H. Abdou, Multi-objective components assignment problem subject to lead-time constraints, Indian Journal of Science and Technology, 11 (2018), 1-9.   Google Scholar

[8]

M. R. Hassan, Solving a component assignment problem for a stochastic-flow network under lead-time constraint, Indian Journal of Science and Technology, 8 (2015), 1-5.   Google Scholar

[9]

M. R. Hassan, Solving flow allocation problems and optimizing system reliability of multisource multisink stochastic flow network, The International Arab Journal of Information Technology (IAJIT), 13 (2016), 477-483.   Google Scholar

[10]

C. C. Hsieh and Y. T. Chen, Reliable and economic resource allocation in an unreliable flow network, Computers and Operations Research, 32 (2005), 613-628.   Google Scholar

[11]

C. C. Hsieh and Y. T. Chen, Simple algorithms for updating multi-resource allocations in an unreliable flow network, Computers and Industrial Engineering, 50 (2006), 120-129.   Google Scholar

[12]

C. C. Hsieh and M. H. Lin, Reliability-oriented multi-resource allocation in a stochastic-flow network, Reliability Engineering and System Safety, 81 (2003), 155-161.   Google Scholar

[13]

Y.-K. Lin and C. T. Yeh, A two-stage approach for a multi-objective component assignment problem for a stochastic-flow network, Engineering Optimization, 45 (2013), 265-285.  doi: 10.1080/0305215X.2012.669381.  Google Scholar

[14]

Y. K. Lin and C. T. Yeh, System reliability maximization for a computer network by finding the optimal two-class allocation subject to budget, Applied Soft Computing, 36 (2015), 578-588.   Google Scholar

[15]

Y. K. Lin and C. T. Yeh, Determining the optimal double-component assignment for a stochastic computer network, Omega, 40 (2012), 120-130.   Google Scholar

[16]

Y. K. Lin and C. T. Yeh, Evaluation of optimal network reliability under components-assignments subject to transmission budget, IEEE Transactions on Reliability, 59 (2010), 539-550.   Google Scholar

[17]

Y. K. Lin and C. T. Yeh, Maximal network reliability with optimal transmission line assignment for stochastic electric power networks via genetic algorithms, Applied Soft Computing, 11 (2011), 2714-2724.   Google Scholar

[18]

Y. K. Lin and C. T. Yeh, Multi-objective optimization for stochastic computer networks using NSGA-Ⅱ and TOPSIS, European Journal of Operational Research, 218 (2012), 735-746.  doi: 10.1016/j.ejor.2011.11.028.  Google Scholar

[19]

Y. K. Lin and C. T. Yeh, Multistate components assignment problem with optimal network reliability subject to assignment budget, Applied Mathematics and Computation, 217 (2011), 10074-10086.  doi: 10.1016/j.amc.2011.05.001.  Google Scholar

[20]

Y. K. Lin and C. T. Yeh, Optimal resource assignment to maximize multistate network reliability for a computer network, Computers and Operations Research, 37 (2010), 2229-2238.  doi: 10.1016/j.cor.2010.03.013.  Google Scholar

[21]

Y. K. Lin and C. T. Yeh, Computer network reliability optimization under double-source assignments subject to transmission budget, Information Sciences, 181 (2011), 582-599.   Google Scholar

[22]

Y. K. LinC. T. Yeh and P. S. Huang, A hybrid ant-tabu algorithm for solving a multistate flow network reliability maximization problem, Applied Soft Computing, 13 (2013), 3529-3543.   Google Scholar

[23]

Q. LiuH. Z. Xiaoxian and Q. Zhao, Genetic algorithm-based study on flow allocation in a multicommodity stochastic-flow network with unreliable nodes, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 8 (2007), 576-581.   Google Scholar

[24]

Q. LiuQ. Z. Zhao and W. K. Zang, Study on multi-objective optimization of flow allocation in a multi-commodity stochastic-flow network with unreliable nodes, Journal of Applied Mathematics Computing (JAMC), 28 (2008), 185-198.  doi: 10.1007/s12190-008-0093-9.  Google Scholar

[25]

M. J. ZuoZ. Tian and H. Z. Huang, An efficient method for reliability evaluation of multistate networks given all minimal path vectors, IIE Transactions, 39 (2007), 811-817.   Google Scholar

Figure 1.  Modified uniform crossover
Figure 2.  Mutation operation
Figure 3.  Crossover operation
Figure 4.  Mutation operation
Figure 5.  Network with three source and two sink nodes
Figure 6.  The minimum cost found at each generation
Figure 7.  Two-source two-sink computer network
Figure 8.  The minimum cost found at each generation
Figure 9.  Network with two source and three sink nodes
Figure 10.  The minimum cost found at each generation
Table 1.  Component information for the network in Figure 5
p Capacity Cost
0 1 2 3 4
1 0.02 0.04 0.14 0.80 0.00 1
2 0.04 0.06 0.10 0.15 0.65 3
3 0.02 0.03 0.05 0.90 0.00 4
4 0.05 0.08 0.87 0.00 0.00 2
5 0.01 0.04 0.10 0.85 0.00 3
6 0.02 0.05 0.15 0.78 0.00 2
7 0.05 0.10 0.85 0.00 0.00 1
8 0.04 0.06 0.15 0.75 0.00 4
9 0.03 0.05 0.12 0.80 0.00 1
10 0.01 0.04 0.05 0.15 0.75 3
11 0.03 0.05 0.07 0.85 0.00 1
12 0.01 0.02 0.07 0.90 0.00 1
p Capacity Cost
0 1 2 3 4
1 0.02 0.04 0.14 0.80 0.00 1
2 0.04 0.06 0.10 0.15 0.65 3
3 0.02 0.03 0.05 0.90 0.00 4
4 0.05 0.08 0.87 0.00 0.00 2
5 0.01 0.04 0.10 0.85 0.00 3
6 0.02 0.05 0.15 0.78 0.00 2
7 0.05 0.10 0.85 0.00 0.00 1
8 0.04 0.06 0.15 0.75 0.00 4
9 0.03 0.05 0.12 0.80 0.00 1
10 0.01 0.04 0.05 0.15 0.75 3
11 0.03 0.05 0.07 0.85 0.00 1
12 0.01 0.02 0.07 0.90 0.00 1
Table 2.  The initial population for the network example in Figure 5
No. The components$ \boldsymbol{(}\mathcal{B} $) The Flow vector (F) $ {\boldsymbol{Z}}_{\boldsymbol{obj}}\left(\mathcal{B}\right) $ $ \boldsymbol{R}\boldsymbol{(}{\boldsymbol{X}}_{\boldsymbol{F}}\boldsymbol{(}\mathcal{B}\boldsymbol{)} $
1 2 10 3 12 7 1 8 11 6 9 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 21.0067 0.259087
2 1 7 4 11 6 8 9 10 2 3 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 22.0005 0.288896
3 6 12 8 11 1 10 9 3 2 4 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 22.0164 0.236032
4 12 6 9 7 11 1 3 8 10 2 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 21.0002 0.293038
5 8 5 4 9 7 1 12 6 10 2 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 21.0036 0.269935
6 10 7 12 9 1 5 6 3 2 4 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 21.0002 0.292652
7 1 9 6 10 5 3 11 4 2 12 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 21.0000 0.312330
8 2 1 9 8 12 4 3 6 10 11 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 22.0012 0.282898
9 9 3 12 5 4 1 6 10 2 7 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 21.0002 0.293636
10 2 8 4 1 7 5 11 9 10 3 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 23.0000 0.314006
No. The components$ \boldsymbol{(}\mathcal{B} $) The Flow vector (F) $ {\boldsymbol{Z}}_{\boldsymbol{obj}}\left(\mathcal{B}\right) $ $ \boldsymbol{R}\boldsymbol{(}{\boldsymbol{X}}_{\boldsymbol{F}}\boldsymbol{(}\mathcal{B}\boldsymbol{)} $
1 2 10 3 12 7 1 8 11 6 9 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 21.0067 0.259087
2 1 7 4 11 6 8 9 10 2 3 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 22.0005 0.288896
3 6 12 8 11 1 10 9 3 2 4 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 22.0164 0.236032
4 12 6 9 7 11 1 3 8 10 2 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 21.0002 0.293038
5 8 5 4 9 7 1 12 6 10 2 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 21.0036 0.269935
6 10 7 12 9 1 5 6 3 2 4 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 21.0002 0.292652
7 1 9 6 10 5 3 11 4 2 12 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 21.0000 0.312330
8 2 1 9 8 12 4 3 6 10 11 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 22.0012 0.282898
9 9 3 12 5 4 1 6 10 2 7 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 21.0002 0.293636
10 2 8 4 1 7 5 11 9 10 3 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 1 23.0000 0.314006
Table 3.  The component information for the network of Figure 7.
p Capacity
0 1 2 3 4 5 6 7 8 9
1 0.001 0.001 0.003 0.004 0.005 0.005 0.006 0.007 0.010 0.015
2 0.001 0.003 0.003 0.004 0.005 0.007 0.007 0.008 0.009 0.010
3 0.002 0.002 0.003 0.006 0.007 0.007 0.010 0.012 0.015 0.017
4 0.001 0.001 0.002 0.003 0.005 0.008 0.010 0.011 0.012 0.015
5 0.001 0.002 0.009 0.012 0.020 0.040 0.050 0.060 0.806 0.000
6 0.001 0.002 0.002 0.005 0.010 0.012 0.015 0.017 0.020 0.025
7 0.001 0.001 0.002 0.005 0.008 0.010 0.012 0.015 0.015 0.017
8 0.001 0.002 0.005 0.005 0.007 0.008 0.010 0.012 0.015 0.015
9 0.001 0.001 0.002 0.002 0.003 0.004 0.005 0.008 0.009 0.010
10 0.002 0.003 0.005 0.006 0.007 0.009 0.012 0.015 0.941 0.000
11 0.002 0.002 0.003 0.005 0.007 0.008 0.010 0.011 0.020 0.030
12 0.001 0.002 0.003 0.005 0.008 0.009 0.010 0.012 0.015 0.040
13 0.001 0.001 0.003 0.005 0.005 0.010 0.011 0.017 0.018 0.020
14 0.001 0.001 0.002 0.002 0.003 0.005 0.007 0.009 0.016 0.021
15 0.001 0.001 0.002 0.003 0.004 0.005 0.007 0.008 0.009 0.011
16 0.001 0.002 0.002 0.004 0.005 0.006 0.007 0.009 0.014 0.017
17 0.001 0.001 0.002 0.002 0.003 0.004 0.005 0.007 0.009 0.011
18 0.001 0.001 0.002 0.002 0.002 0.003 0.003 0.004 0.005 0.007
19 0.001 0.001 0.002 0.003 0.005 0.008 0.009 0.011 0.013 0.014
20 0.002 0.002 0.003 0.006 0.007 0.007 0.010 0.013 0.015 0.020
10 11 12 13 14 15 16 17 18 19 20
0.060 0.150 0.733 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.943 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.919 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.015 0.016 0.020 0.856 0.025 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.891 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.020 0.022 0.025 0.030 0.817 0.000 0.000 0.000 0.000 0.000 0.000
0.016 0.020 0.884 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.011 0.015 0.016 0.017 0.019 0.020 0.857 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.902 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.895 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.025 0.031 0.853 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.024 0.025 0.030 0.035 0.040 0.060 0.719 0.000 0.000 0.000 0.000
0.015 0.017 0.020 0.027 0.870 0.000 0.000 0.000 0.000 0.000 0.000
0.020 0.022 0.025 0.030 0.035 0.040 0.761 0.000 0.000 0.000 0.000
0.015 0.017 0.018 0.019 0.020 0.022 0.844 0.017 0.017 0.000 0.000
0.008 0.009 0.011 0.013 0.014 0.014 0.015 0.000 0.000 0.019 0.020
0.015 0.017 0.020 0.030 0.851 0.000 0.000 0.000 0.000 0.000 0.000
0.915 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
21 22 23 24 25 26 27 28 29 30 31
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.019 0.020 0.023 0.025 0.026 0.740 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
p Capacity
0 1 2 3 4 5 6 7 8 9
1 0.001 0.001 0.003 0.004 0.005 0.005 0.006 0.007 0.010 0.015
2 0.001 0.003 0.003 0.004 0.005 0.007 0.007 0.008 0.009 0.010
3 0.002 0.002 0.003 0.006 0.007 0.007 0.010 0.012 0.015 0.017
4 0.001 0.001 0.002 0.003 0.005 0.008 0.010 0.011 0.012 0.015
5 0.001 0.002 0.009 0.012 0.020 0.040 0.050 0.060 0.806 0.000
6 0.001 0.002 0.002 0.005 0.010 0.012 0.015 0.017 0.020 0.025
7 0.001 0.001 0.002 0.005 0.008 0.010 0.012 0.015 0.015 0.017
8 0.001 0.002 0.005 0.005 0.007 0.008 0.010 0.012 0.015 0.015
9 0.001 0.001 0.002 0.002 0.003 0.004 0.005 0.008 0.009 0.010
10 0.002 0.003 0.005 0.006 0.007 0.009 0.012 0.015 0.941 0.000
11 0.002 0.002 0.003 0.005 0.007 0.008 0.010 0.011 0.020 0.030
12 0.001 0.002 0.003 0.005 0.008 0.009 0.010 0.012 0.015 0.040
13 0.001 0.001 0.003 0.005 0.005 0.010 0.011 0.017 0.018 0.020
14 0.001 0.001 0.002 0.002 0.003 0.005 0.007 0.009 0.016 0.021
15 0.001 0.001 0.002 0.003 0.004 0.005 0.007 0.008 0.009 0.011
16 0.001 0.002 0.002 0.004 0.005 0.006 0.007 0.009 0.014 0.017
17 0.001 0.001 0.002 0.002 0.003 0.004 0.005 0.007 0.009 0.011
18 0.001 0.001 0.002 0.002 0.002 0.003 0.003 0.004 0.005 0.007
19 0.001 0.001 0.002 0.003 0.005 0.008 0.009 0.011 0.013 0.014
20 0.002 0.002 0.003 0.006 0.007 0.007 0.010 0.013 0.015 0.020
10 11 12 13 14 15 16 17 18 19 20
0.060 0.150 0.733 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.943 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.919 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.015 0.016 0.020 0.856 0.025 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.891 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.020 0.022 0.025 0.030 0.817 0.000 0.000 0.000 0.000 0.000 0.000
0.016 0.020 0.884 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.011 0.015 0.016 0.017 0.019 0.020 0.857 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.902 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.895 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.025 0.031 0.853 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.024 0.025 0.030 0.035 0.040 0.060 0.719 0.000 0.000 0.000 0.000
0.015 0.017 0.020 0.027 0.870 0.000 0.000 0.000 0.000 0.000 0.000
0.020 0.022 0.025 0.030 0.035 0.040 0.761 0.000 0.000 0.000 0.000
0.015 0.017 0.018 0.019 0.020 0.022 0.844 0.017 0.017 0.000 0.000
0.008 0.009 0.011 0.013 0.014 0.014 0.015 0.000 0.000 0.019 0.020
0.015 0.017 0.020 0.030 0.851 0.000 0.000 0.000 0.000 0.000 0.000
0.915 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
21 22 23 24 25 26 27 28 29 30 31
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.019 0.020 0.023 0.025 0.026 0.740 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
[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]

Peijun Li, Ganghua Yuan. Increasing stability for the inverse source scattering problem with multi-frequencies. Inverse Problems & Imaging, 2017, 11 (4) : 745-759. doi: 10.3934/ipi.2017035

[3]

Martin Gugat, Alexander Keimer, Günter Leugering, Zhiqiang Wang. Analysis of a system of nonlocal conservation laws for multi-commodity flow on networks. Networks & Heterogeneous Media, 2015, 10 (4) : 749-785. doi: 10.3934/nhm.2015.10.749

[4]

Omer Faruk Yilmaz, Mehmet Bulent Durmusoglu. A performance comparison and evaluation of metaheuristics for a batch scheduling problem in a multi-hybrid cell manufacturing system with skilled workforce assignment. Journal of Industrial & Management Optimization, 2018, 14 (3) : 1219-1249. doi: 10.3934/jimo.2018007

[5]

Andrea Picco, Lamberto Rondoni. Boltzmann maps for networks of chemical reactions and the multi-stability problem. Networks & Heterogeneous Media, 2009, 4 (3) : 501-526. doi: 10.3934/nhm.2009.4.501

[6]

Loc H. Nguyen, Qitong Li, Michael V. Klibanov. A convergent numerical method for a multi-frequency inverse source problem in inhomogenous media. Inverse Problems & Imaging, 2019, 13 (5) : 1067-1094. doi: 10.3934/ipi.2019048

[7]

Kien Ming Ng, Trung Hieu Tran. A parallel water flow algorithm with local search for solving the quadratic assignment problem. Journal of Industrial & Management Optimization, 2019, 15 (1) : 235-259. doi: 10.3934/jimo.2018041

[8]

Zhiping Chen, Jia Liu, Gang Li. Time consistent policy of multi-period mean-variance problem in stochastic markets. Journal of Industrial & Management Optimization, 2016, 12 (1) : 229-249. doi: 10.3934/jimo.2016.12.229

[9]

Gunduz Caginalp, Mark DeSantis. Multi-group asset flow equations and stability. Discrete & Continuous Dynamical Systems - B, 2011, 16 (1) : 109-150. doi: 10.3934/dcdsb.2011.16.109

[10]

Giulia Cavagnari, Antonio Marigonda, Benedetto Piccoli. Optimal synchronization problem for a multi-agent system. Networks & Heterogeneous Media, 2017, 12 (2) : 277-295. doi: 10.3934/nhm.2017012

[11]

Massimiliano Caramia, Giovanni Storchi. Evaluating the effects of parking price and location in multi-modal transportation networks. Networks & Heterogeneous Media, 2006, 1 (3) : 441-465. doi: 10.3934/nhm.2006.1.441

[12]

Emiliano Cristiani, Elisa Iacomini. An interface-free multi-scale multi-order model for traffic flow. Discrete & Continuous Dynamical Systems - B, 2019, 24 (11) : 6189-6207. doi: 10.3934/dcdsb.2019135

[13]

Felisia Angela Chiarello, Paola Goatin. Non-local multi-class traffic flow models. Networks & Heterogeneous Media, 2019, 14 (2) : 371-387. doi: 10.3934/nhm.2019015

[14]

Bettina Klaus, Frédéric Payot. Paths to stability in the assignment problem. Journal of Dynamics & Games, 2015, 2 (3&4) : 257-287. doi: 10.3934/jdg.2015004

[15]

Djano Kandaswamy, Thierry Blu, Dimitri Van De Ville. Analytic sensing for multi-layer spherical models with application to EEG source imaging. Inverse Problems & Imaging, 2013, 7 (4) : 1251-1270. doi: 10.3934/ipi.2013.7.1251

[16]

Rui Wang, Xiaoyue Li, Denis S. Mukama. On stochastic multi-group Lotka-Volterra ecosystems with regime switching. Discrete & Continuous Dynamical Systems - B, 2017, 22 (9) : 3499-3528. doi: 10.3934/dcdsb.2017177

[17]

Michel Cristofol, Jimmy Garnier, François Hamel, Lionel Roques. Uniqueness from pointwise observations in a multi-parameter inverse problem. Communications on Pure & Applied Analysis, 2012, 11 (1) : 173-188. doi: 10.3934/cpaa.2012.11.173

[18]

Yuzhong Zhang, Fan Zhang, Maocheng Cai. Some new results on multi-dimension Knapsack problem. Journal of Industrial & Management Optimization, 2005, 1 (3) : 315-321. doi: 10.3934/jimo.2005.1.315

[19]

Yong Zhang, Xingyu Yang, Baixun Li. Distribution-free solutions to the extended multi-period newsboy problem. Journal of Industrial & Management Optimization, 2017, 13 (2) : 633-647. doi: 10.3934/jimo.2016037

[20]

Xueyong Wang, Yiju Wang, Gang Wang. An accelerated augmented Lagrangian method for multi-criteria optimization problem. Journal of Industrial & Management Optimization, 2020, 16 (1) : 1-9. doi: 10.3934/jimo.2018136

2018 Impact Factor: 1.025

Metrics

  • PDF downloads (16)
  • HTML views (27)
  • Cited by (0)

Other articles
by authors

[Back to Top]