Transmission quality of a communication/computer system is a high-level objective of system supervisors. Therefore, transmission reliability improvement or optimization is an important issue for many organizations. One way to maximize transmission reliability is to model the system as a stochastic communication network including arcs and nodes and then determine the optimal component redundancy allocation. However, modern components are highly reliable. Thus, a decision maker may be more concerned about cost than reliability. This article considers cost-oriented component allocation subject to a reliability threshold and correlated failures characterized by a correlated binomial distribution model. To solve this problem, we employ a genetic algorithm to search for the optimal component redundancy allocation possessing minimal allocation cost. The computational efficiency of the genetic algorithm–based method is demonstrated through several benchmark networks and compared against several popular soft computing algorithms.
Ramirez-MarquezJERoccoSCM. Stochastic network interdiction optimization via capacitated network reliability modeling and probabilistic solution discovery. Reliab Eng Syst Safe2009; 94: 913–921.
2.
LinYKYehCT. Using minimal cuts to optimize network reliability for a stochastic computer network subject to assignment budget. Comput Oper Res2011; 38: 1175–1187.
3.
LinYKYehCTHuangPS. A hybrid ant-tabu algorithm for solving a multistate flow network reliability maximization problem. Appl Soft Comput2013; 13: 3529–3543.
4.
YehCTFiondellaL. Optimal redundancy allocation to maximize multi-state computer network reliability subject to correlated failures. Reliab Eng Syst Safe2017; 166: 138–150.
5.
MisraKBSharmaJ. A new geometric programming formulation for a reliability problem. Int J Control1973; 18: 497–503.
6.
RoyPMahapatraBSMahapatraGSet al. Entropy based region reducing genetic algorithm for reliability redundancy allocation in interval environment. Expert Syst Appl2014; 41: 6147–6160.
7.
YehCTLinYK. Component allocation cost minimization for a multistate computer network subject to a reliability threshold using tabu search. J Ind Manag Optim2016; 12: 141–167.
8.
TianZZuoMJHuangH. Reliability-redundancy allocation for multi-state series-parallel systems. IEEE T Reliab2008; 57: 303–310.
9.
AzaronAPerkgozCKatagiriHet al. Multi-objective reliability optimization for dissimilar-unit cold-standby systems using a genetic algorithm. Comput Oper Res2009; 36: 1562–1571.
10.
SoltaniRSadjadiSJ. Reliability optimization through robust redundancy allocation models with choice of component type under fuzziness. Proc IMechE, Part O: J Risk and Reliability2014; 228: 449–459.
11.
LinYK. A simple algorithm for reliability evaluation of a stochastic-flow network with node failure. Comput Oper Res2001; 28: 1277–1285.
12.
LinYKHuangCF. Assessing reliability within error rate and time constraint for a stochastic node-imperfect computer network. Proc IMechE, Part O: J Risk and Reliability2013; 227: 80–85.
13.
LinYKHuangCF. Stochastic computer network under accuracy rate constraint from QoS viewpoint. Inform Sciences2013; 239: 241–252.
14.
ZhangYXuZWangXet al. Single minimal path based backup path for multi-state network. Proc IMechE, Part O: J Risk and Reliability2014; 228: 152–165.
15.
JaneCCLaihYW. Distribution and reliability evaluation of max-flow in dynamic multi-state flow networks. Eur J Oper Res2017; 259: 1045–1053.
16.
LevitinGLisnianskiA. A new approach to solving problems of multi-state system reliability optimization. Qual Reliab Eng Int2001; 17: 93–104.
17.
FiondellaL. Reliability and sensitivity analysis of coherent systems with negatively correlated component failures. Int J Reliab Qual Saf Eng2010; 17: 505–529.
18.
LinYKChangPCFiondellaL. Quantifying the impact of correlated failures on stochastic flow network reliability. IEEE T Reliab2012; 61: 692–701.
19.
ColbournCJ. The combinatorics of network reliability. New York: Oxford University Press, 1987.
20.
ChambariANajafiAARahmatiSHAet al. An efficient simulated annealing algorithm for the redundancy allocation problem with a choice of redundancy strategies. Reliab Eng Syst Safe2013; 119: 158–164.
21.
HePWuKXuJet al. Multilevel redundancy allocation using two dimensional arrays encoding and hybrid genetic algorithm. Comput Ind Eng2013; 64: 69–83.
22.
KanagarajGPonnambalamSGJawaharN. A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems. Comput Ind Eng2013; 66: 1115–1124.
23.
GhorabaeeMKAmiriMAzimiP. Genetic algorithm for solving bi-objective redundancy allocation problem with k-out-of-n subsystems. Appl Math Model2015; 39: 6396–6409.
24.
KimHKimP. Reliability–redundancy allocation problem considering optimal redundancy strategy using parallel genetic algorithm. Reliab Eng Syst Safe2017; 159: 153–160.
25.
FordLRFulkersonDR. Flows in networks. Princeton, NJ: Princeton University, 1962.
26.
ZuoMJTianZHuangHZ. An efficient method for reliability evaluation of multistate networks given all minimal path vectors. IIE Trans2007; 39: 811–817.
27.
ZachariaPMentiAZachariasT. Genetic algorithm-based optimal design of shunt compensators in the presence of harmonics. Electr Pow Syst Res2008; 78: 728–735.
28.
SrinivasMLalitMP. Genetic algorithms: a survey. Computer1994; 27: 17–26.
29.
Ramirez-MarquezJECoitDW. A Monte-Carlo simulation approach for approximating multi-state two-terminal reliability. Reliab Eng Syst Safe2005; 87: 253–264.
30.
AmiriAPirkulH. Routing and capacity assignment in backbone communication networks. Comput Oper Res1997; 24: 275–287.
31.
Rahnamay-NaeiniMPezoaJEAzarGet al. Modeling stochastic correlated Failures and their effects on network reliability. In: Proceedings of the 20th international conference on computer communications and networks (ICCCN), Maui, HI, 31 July–4 August 2011, pp.1–6. New York: IEEE.
32.
GrefenstetteJJ. Optimization of control parameters for genetic algorithms. IEEE T Syst Man Cyb1986; 16: 122–128.
33.
MitchellM. An introduction to genetic algorithms. Cambridge, MA: MIT Press, 1996.
34.
SchafferJDCaruanaRAEshelmanLJet al. A study of control parameters affecting online performance of genetic algorithms for function optimization. In: Proceedings of the 3rd international conference on genetic algorithms, George Mason University, Fairfax, VA, 4–7 June 1989, pp.51–60. Los Altos, CA: Morgan Kaufmann.
35.
ManKFTangKSKwongS. Genetic algorithms: concepts and applications. IEEE T Ind Electron1996; 43: 519–534.
36.
YangQZhangCWangC. An efficient discrete particle swarm algorithm for task assignment problems. In: Proceedings of the 2009 IEEE international conference on granular computing, Nanchang, China, 17–19 August 2009, pp.686–690. New York: IEEE.
37.
JamesTRegoCGloverF. A cooperative parallel tabu search algorithm for the quadratic assignment problem. Eur J Oper Res2009; 195: 810–826.
38.
YehCTLinYKYangJY. Network reliability maximization for stochastic-flow network subject to correlated failures using genetic algorithm and tabu search. Eng Optimiz. Epub ahead of print 28July2017. DOI: 10.1080/0305215X.2017.1353089.
