Optimal redundancy allocation in complex systems

Abstract

PurposeThis paper aims to focus on solving highly constrained redundancy optimization problems in binary complex systems.Design/methodology/approachThe proposed algorithm searches a possibly improved solution in the k‐neighborhood (k≥2) of the current best feasible solution, by adding one unit in a selected subsystem and eliminating one from some other subsystem(s).FindingsThe algorithm is tested on complex system structures from the literature by solving a set of problems (with both linear and non‐linear constraints), with given and randomly generated data. It is observed that, compared with the other existing heuristics, there is much overall improvement in various performance measures.Practical implicationsThe proposed algorithm is a better alternative and can be easily and efficiently applied to numerous real life systems such as computer and communication systems, telecommunication networks, automobile, nuclear and defense systems etc., giving optimal/near‐optimal solutions.Originality/valueResearchers in reliability optimization have placed emphasis on heuristic approaches. The paper presents a new heuristic algorithm for solving the constrained redundancy optimization problems in complex binary systems.

Software Architecture Optimization Methods: A Systematic Literature Review

“…Performance(84) EXACT PROBLEM-SPECIFIC(3) [1], [112], [126], EXACT STANDARD(14) [12]- [15], [42]- [44], [69], [117], [169], [174], [218], [236], [241], GENERAL(1) [153], METAHEURIS-TIC(45) [2], [3], [23], [29], [32], [35], [38]- [40], [45], [46], [66], [73], [77], [83], [84], [88], [114], [122]- [124], [130], [133], [141], [143], [145], [160], [161], [170], [175]- [177], [188], [189], [196]- [198], [200], [220], …”

Genetic algorithm for chance constrained reliability stochastic optimisation problems

“…Tian and Zuo (2008) presented a model for a multi-state seriesparallel system to jointly determine the optimal component state distribution and optimal redundancy for each stage. Kumar et al (2010) and Agarwal et al (2010) presented new heuristic methods for solving constrained RAPs in varied complex systems, such as computer networks, communication systems, transportation systems, etc. To address the uncertainty factor in the field of reliability-based design optimisation, Robert and Stephen (2009) presented a decision-analytic approach, an alternative to chance constrained approach, to optimise an objective function subject to uncertainties about design constraints.…”

Minimal path set importance in complex systems

“…Agarwal and Aggarwal 16 showed that their algorithms perform better than those in Kohda and Inoue and Shi 18,19 and provide attractive alternatives to that of Kim and Yum. 20 Recently, Aggarwal 30 proposed a heuristic algorithm based on 3-neighborhood, which is an improvement over Agarwal et al 25…”

“…A huge number of techniques, including heuristic and metaheuristic, have been developed for solving redundancy allocation problems for complex systems in the literature. 16,[18][19][20][21][22][23][24][25][26][27][28][29][30] Shi 19 proposed a heuristic algorithm to solve problems of redundancy allocation based on minimal path sets. Here, the feasible solutions obtained in successive iterations are in 1-neighborhood or 2-neighborhoods of each other.…”

“…Agarwal and Aggarwal 16 showed that their algorithms perform better than those in Kohda and Inoue and Shi 18,19 and provide attractive alternatives to that of Kim and Yum. 20 Recently, Aggarwal 30 proposed a heuristic algorithm based on 3-neighborhood, which is an improvement over Agarwal et al 25 For the broadly considered issue of redundancy allocation in complex systems, we introduce a heuristic algorithm P-Alg 2 that does not require the constraints to be separable and monotonically non-decreasing. The algorithm P-Alg 2 requires determining all minimal path sets of the system following an approach of adjusting unit-increment with time.…”