Solving the redundancy allocation problem using a combined neural network/genetic algorithm approach
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Penalty guided genetic search for reliability design optimization
Computers and Industrial Engineering
Computers and Operations Research
Variable neighborhood search for the degree-constrained minimum spanning tree problem
Discrete Applied Mathematics - Special issue: Third ALIO-EURO meeting on applied combinatorial optimization
An efficient heuristic for series-parallel redundant reliability problems
Computers and Operations Research
Immune algorithms-based approach for redundant reliability problems with multiple component choices
Computers in Industry - Special issue: Application of genetics algorithms in industry
New Multiobjective Metaheuristic Solution Procedures for Capital Investment Planning
Journal of Heuristics
An efficient variable neighborhood search heuristic for very large scale vehicle routing problems
Computers and Operations Research
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Multicriteria assignment problem (selection of access points)
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
A bi-objective iterated local search heuristic with path-relinking for the p-median problem
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Electronic Notes in Theoretical Computer Science (ENTCS)
Variable neighborhood search for drilling operation scheduling in PCB industries
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
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A variable neighborhood search (VNS) algorithm has been developed to solve the multiple objective redundancy allocation problems (MORAP). The single objective RAP is to select the proper combination and redundancy levels of components to meet system level constraints, and to optimize the specified objective function. In practice, the need to consider two or more conflicting objectives simultaneously increases nowadays in order to assure managers or designers' demand. Amongst all system level objectives, maximizing system reliability is the most studied and important one, while system weight or system cost minimization are two other popular objectives to consider. According to the authors' experience, VNS has successfully solved the single objective RAP (Liang and Chen, Reliab. Eng. Syst. Saf. 92:323---331, 2007; Liang et al., IMA J. Manag. Math. 18:135---155, 2007). Therefore, this study aims at extending the single objective VNS algorithm to a multiple objective version for solving multiple objective redundancy allocation problems. A new selection strategy of base solutions that balances the intensity and diversity of the approximated Pareto front is introduced. The performance of the proposed multi-objective VNS algorithm (MOVNS) is verified by testing on three sets of complex instances with 5, 14 and 14 subsystems respectively. While comparing to the leading heuristics in the literature, the results show that MOVNS is able to generate more non-dominated solutions in a very efficient manner, and performs competitively in all performance measure categories. In other words, computational results reveal the advantages and benefits of VNS on solving multi-objective RAP.