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Journal ArticleDOI

Optimal Allocation of Redundant Components for Large Systems

TLDR
Two new exact algorithms are presented for the case where the objective and constraint functions are stagewise separable, both are branch and bound algorithms.
Abstract
This paper discusses allocating redundant components subject to resource constraints so as to optimize some measure of system performance. Two new exact algorithms are presented for the case where the objective and constraint functions are stagewise separable. Both are branch and bound algorithms. The first, BLE1, is based on an underlying knapsack structure, while the second, BLE2, exploits a multiple-choice knapsack structure. BLE1 can solve problems with 100 stages and 10 constraints in just a few seconds of CPU time on an IBM 3033. For larger problems, BLH, a heuristic procedure, is proposed. The heuristic is based on the ``slippery algorithm'' for knapsack problems. Computational testing indicates BLH often finds the optimal solution, and when it doesn't, the solutions are quite close to optimal.

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Citations
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Journal ArticleDOI

Reliability optimization of series-parallel systems using a genetic algorithm

TL;DR: A problem-specific genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine the optimal design configuration when there are multiple component choices available for each of several k-out-of-n:G subsystems.
Journal ArticleDOI

An ant colony optimization algorithm for the redundancy allocation problem (RAP)

TL;DR: An ant colony meta-heuristic optimization method is used to solve the redundancy allocation problem (RAP), and it is shown that the ant colony method performs with little variability over problem instance or random number seed.
Journal ArticleDOI

Efficiently solving the redundancy allocation problem using tabu search

TL;DR: In this article, a Tabu search meta-heuristic has been developed and successfully demonstrated to provide solutions to the system reliability optimization problem of redundancy allocation, which generally involves the selection of components and redundancy levels to maximize system reliability given various system-level constraints.
Journal ArticleDOI

Penalty guided genetic search for reliability design optimization

TL;DR: A penalty guided genetic algorithm which efficiently and effectively searches over promising feasible and infeasible regions to identify a final, feasible optimal, or near optimal, solution.
References
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Journal ArticleDOI

The Lagrangian Relaxation Method for Solving Integer Programming Problems

TL;DR: This paper is a review of Lagrangian relaxation based on what has been learned in the last decade and has led to dramatically improved algorithms for a number of important problems in the areas of routing, location, scheduling, assignment and set covering.
Journal ArticleDOI

An Algorithm for Large Zero-One Knapsack Problems

Egon Balas, +1 more
- 01 Oct 1980 - 
TL;DR: An algorithm for the 0-1 knapsack problem (KP), which relies mainly on three new ideas, one of which is a binary search-type procedure for solving LKP which, unlike earlier methods, does not require any ordering of the variables.
Journal ArticleDOI

Integer Programming Algorithms: A Framework and State-of-the-Art Survey

TL;DR: A unifying framework is developed to facilitate the understanding of most known computational approaches to integer programming, and a number of currently operational algorithms are related to this framework.
Journal ArticleDOI

The Multiple-Choice Knapsack Problem

TL;DR: The branch-and-bound algorithm for the multiple-choice knapsack problem resides with the quick solution of the linear programming relaxation and its efficient, subsequent reoptimization as a result of branching.
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