Matrix analysis
Introduction to operations research, 4th ed.
Introduction to operations research, 4th ed.
Penalty guided genetic search for reliability design optimization
Computers and Industrial Engineering
Genetic algorithm for non-linear mixed integer programming problems and its applications
Computers and Industrial Engineering
Tabu Search
Operations Research Letters
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
Reliability of grid service systems
Computers and Industrial Engineering
Economic analysis of an n-unit parallel redundant system based on a Stackelberg game formulation
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
An efficient heuristic for reliability design optimization problems
Computers and Operations Research
A cross entropy based algorithm for reliability problems
Journal of Heuristics
Reliability of grid service systems
Computers and Industrial Engineering
Immune algorithms-based approach for redundant reliability problems with multiple component choices
Computers in Industry - Special issue: Application of genetics algorithms in industry
Penalty guided PSO for reliability design problems
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Expert Systems with Applications: An International Journal
Solving reliability redundancy allocation problems using an artificial bee colony algorithm
Computers and Operations Research
Expert Systems with Applications: An International Journal
Computers and Operations Research
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This paper investigates the series-parallel redundant reliability problems subject to multiple separable linear constraints, where each subsystem has multiple component choices. The problems generalize the typical series-parallel reliability problems when the number of component choices for each subsystem is set to one. Instead of conventional approaches, e.g. dynamic programming, geometric programming and piecewise linear approximation, a simple linear programming approach is proposed to approximate the integer nonlinear programming problem. Numerical results for test problems with single (multiple) component choice(s) are reported and compared. Limited numerical results demonstrate the efficiency and the effectiveness of the proposed approach. Additionally, results obtained from the approach proposed herein might provide an effective lower bound for branch-and-bound methods to obtain the global optimum for the problem.