W. Kuo, R. Wan - Recent Advances in Optimal Reliability Allocation, страница 7

Availability28Way Kuo and Rui Wanis primarily used as the performance measure of renewable multi-statesystems whose optimal design has been emphasized and well developed inthe past 10 years. Optimal design problems are generally formulated tomaximize an appropriate system performance measure under resource constraints, and more realistic problems involving multi-objective programming are also being considered.When turning to optimization techniques, heuristic, meta-heuristic andexact methods are significantly applied in optimal reliability design.

Recently,many advances in meta-heuristics and exact methods have been reported.Particularly, a new meta-heuristic method called ant colony optimizationhas been introduced and demonstrated to be a very promising generalmethod in this field.

Hybrid GAs may be the most important recent development among the optimization techniques since they retain the advantages of GAs in robustness and feasibility but significantly improve theircomputational efficiency.Optimal reliability design has attracted many researchers, who haveproduced hundreds of publications since 1960.

Due to the increasing complexity of practical engineering systems and the critical importance of reliability in these complex systems, this still seems to be a very fruitful areafor future research.Compared to traditional binary-state systems, there are still many unsolved topics in MSS optimal design including••••using percentile life as a system performance measure;involving cold-standby redundancy;nonrenewable MSS optimal design;applying optimization algorithms other than GAs, especially hybrid optimization techniques.From the view of optimization techniques, there are opportunities forimproved effectiveness and efficiency of reported ACO, TS, IA and GDA,while some new meta-heuristic algorithms such as Harmony Search Algorithm and Particle Swarm Optimization may offer excellent solutions forreliability optimization problems. Hybrid optimization techniques are another very promising general developmental direction in this field. Theymay combine heuristic methods, NN or some local search methods with allkinds of meta-heuristics to improve computational efficiency or with exactmethods to reduce search space.

We may even be able to combine twometa-heuristic algorithms such as GA and SA or ACO.The research dealing with the understanding and application of reliability at the nano level has also demonstrated its attraction and vitality in recent years. Optimal system design that considers reliability within theRecent Advances in Optimal Reliability Allocation29uniqueness of nano-systems has seldom been reported in the literature. Itdeserves a lot more attention in the future.

In addition, uncertainty andcomponent dependency will be critical areas to consider in future researchon optimal reliability design.AcknowledgementThis work was partly supported by NSF Projects DMI-0429176. It is republished with the permission of IEEE Transactions on Systems, Man,Cybernetics, Part A: Systems and Humans.References[1]Altiparmak F, Dengiz B, Smith AE (2003) Optimal design of reliable computer networks: a comparison of meta heuristics. Journal of Heuristics 9:471-487[2] Amari SV, Dugan JB, Misra RB (1999) Optimal reliability of systems subject to imperfect fault-coverage.

IEEE Transactions on Reliability 48: 275284[3] Amari SV, Pham H, Dill G (2004) Optimal design of k-out-of-n: G subsystems subjected to imperfect fault-coverage. IEEE Transactions on Reliability53: 567-575[4] Andrews JD, Bartlett LA (2005) A branching search approach to safety system design optimization. Reliability Engineering & System Safety 87: 23-30[5] Aneja YP, Chandrasekaran R, Nair KPK (2004) Minimal-cost system reliability with discrete-choice sets for components. IEEE Transactions on Reliability 53:71-76[6] Bueno VC (2005) Minimal standby redundancy allocation in a k-out-of-n: Fsystem of dependent components.

European Journal of Operation Research165: 786-793[7] Busacca PG, Marseguerra M, Zio E (2001) Multi-objective optimization bygenetic algorithms: application to safety systems. Reliability Engineering &System Safety 72: 59-74[8] Chern MS (1987) On the computational complexity of reliability redundancyallocation in a series system. Operation Research Letter SE-13: 582-592[9] Chen TC, You PS (2005) Immune algorithms-based approach for redundantreliability problems with multiple component choices.

Computers in Industry56: 195-205[10] Coit DW, Smith AE (1996) Reliability optimization of series-parallel systems using a genetic algorithm. IEEE Transactions on Reliability 45: 25426030Way Kuo and Rui Wan[11] Coit DW, Smith AE (1998) Redundancy allocation to maximize a lower percentile of the system time-to-failure distribution.

IEEE Transactions on Reliability 47: 79-87[12] Coit DW, Liu J (2000) System reliability optimization with k-out-of-n subsystems. International Journal of Reliability, Quality and Safety Engineering7: 129-142[13] Coit DW (2001) Cold-standby redundancy optimization for nonrepairablesystems. IIE Transactions 33: 471-478[14] Coit DW, Smith AE (2002) Genetic algorithm to maximize a lower-boundfor system time-to-failure with uncertain component Weibull parameters.Computer & Industrial Engineering 41: 423-440[15] Coit DW (2003) Maximization of system reliability with a choice of redundancy strategies.

IIE Transactions 35: 535-543[16] Coit DW, Jin T, Wattanapongsakorn N (2004) System optimization withcomponent reliability estimation uncertainty: a multi-criteria approach. IEEETransactions on Reliability 53: 369-380[17] Cui L, Kuo W, Loh HT, Xie M (2004) Optimal allocation of minimal & perfect repairs under resource constraints.

IEEE Transactions on Reliability 53:193-199[18] Dimou CK, Koumousis VK (2003) Competitive genetic algorithms with application to reliability optimal design. Advances in Engineering Software 34:773-785[19] Djerdjour M (1997) An enumerative algorithm framework for a class ofnonlinear programming problems. European Journal of Operation Research101: 101-121[20] Djerdjour M, Rekab K (2001) A branch and bound algorithm for designingreliable systems at a minimum cost.

Applied Mathematics and Computation118: 247-259[21] Dorigo M (1992) Optimization learning and natural algorithm. Ph.D. Thesis,Politecnico di Milano, Italy[22] Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by acolony of cooperating agents. IEEE Transactions on System, Man, and Cybernetics-part B: Cybernetics 26: 29-41[23] Dorigo M, Gambardella LM (1997) Ant colonies for traveling salesmanproblem. BioSystem 43: 73-81[24] Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary computation 1: 53-66[25] Dorigo M, Caro G (1999) The ant colony optimization meta heuristic. In:Corne D, Dorigo M, Glover F (eds.) New Ideas in Optimization.

McGrawHill, pp 11-32.[26] Dorigo M, Caro GD, Gambardella LM (1999) Ant algorithm for discrete optimization. Artificial Life 5: 137-172[27] Elegbede C, Chu C, Adjallah K, Yalaoui F (2003) Reliability allocationthrough cost minimization. IEEE Transactions on Reliability 52: 106-111Recent Advances in Optimal Reliability Allocation31[28] Elegbede C, Adjallah K (2003) Availability allocation to repairable systemswith genetic algorithms: a multi-objective formulation Reliability Engineering and System Safety 82: 319-330[29] Gen M, Kim JR (1999) GA-based reliability design: state-of-the-art survey.Computer & Industrial Engineering 37: 151-155[30] Ha C, Kuo W (2006) Reliability redundancy allocation: an improved realization for nonconvex nonlinear programming problems.

European Journal ofOperational Research 171: 24-38[31] Ha C, Kuo W (accepted) Multi-paths iterative heuristic for redundancy allocation: the tree heuristic. IEEE Transactions on Reliability[32] Ha C, Kuo W (2005) Multi-path approach for Reliability-redundancy allocation using a scaling method. Journal of Heuristics, 11: 201-237[33] Hsieh CC, Hsieh YC (2003) Reliability and cost optimization in distributedcomputing systems.

Computer & Operations Research 30: 1103-1119[34] Hsieh CC (2003) Optimal task allocation and hardware redundancy policiesin distributed computing system. European Journal of Operational Research147: 430-447[35] Hsieh YC, Chen TC, Bricker DL (1998) Genetic algorithm for reliability design problems.