A heuristic method for non-homogeneous redundancy optimization of series-parallel multi-state systems

• Published in: Journal of heuristics Vol. 17; no. 1; pp. 1 - 22
• Main Authors: Ouzineb, Mohamed, Nourelfath, Mustapha, Gendreau, Michel
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.02.2011; Springer Science + Business Media; Springer Nature B.V
• Subjects: Artificial Intelligence; Calculus of Variations and Optimal Control; Optimization; Computer science; Design; Genetic algorithms; Heuristic; Integer programming; Linear programming; Management Science; Mathematics; Mathematics and Statistics; Operations Research; Operations Research/Decision Theory; Optimization; Optimization techniques; Parallel processing; Studies; Canada; Multi-state; Series-parallel systems; Non-homogeneous redundancy optimization; Meta-heuristics
• DOI: 10.1007/s10732-009-9117-3
• ISSN: 1381-1231

This paper develops an efficient heuristic to solve the non-homogeneous redundancy allocation problem for multi-state series-parallel systems. Non identical components can be used in parallel to improve the system availability by providing redundancy in subsystems. Multiple component choices are available for each subsystem. The components are binary and chosen from a list of products available on the market, and are characterized in terms of their cost, performance and availability. The objective is to determine the minimal-cost series-parallel system structure subject to a multi-state availability constraint. System availability is represented by a multi-state availability function, which extends the binary-state availability. This function is defined as the ability to satisfy consumer demand that is represented as a piecewise cumulative load curve. A fast procedure is used, based on universal generating function, to evaluate the multi-state system availability. The proposed heuristic approach is based on a combination of space partitioning, genetic algorithms (GA) and tabu search (TS). After dividing the search space into a set of disjoint subsets, this approach uses GA to select the subspaces, and applies TS to each selected subspace. The design problem, solved in this study, has been previously analyzed using GA. Numerical results for the test problems from previous research are reported, and larger test problems are randomly generated. These results show that the proposed approach is efficient both in terms of both of solution quality and computational time, as compared to existing approaches.