An efficient algorithm to solve integer-programming problems arising in system-reliability design

Misra, Krishna B.; Sharma, Usha

doi:10.1109/24.75341

Cited by 172 publications

(59 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The dummy level techniqueworks on the basis that, if a factor A has two levels A 1 and A 2 , it can be assigned to a three-level column by creating a dummy level A 3 , which could be taken to be the same as either A 1 or A 2 . For a more detailed explanation, refer to reference [18].…”

Section: Matrix Experimentsmentioning

confidence: 99%

Using statistically designed experiments for safety system optimization

Andrews

Bartlett

2004

Proceedings of the Institution of Mechanical Engineers, Part E:

View full text Add to dashboard Cite

This paper describes the method of statistically designed experiments (SDEs) which is used as a structured method to investigate the best setting for a number of decision variables in a system design problem. Traditionally, in the design of safety-critical systems, a trial and error type approach is undertaken to achieve a nal system that meets the design objectives. This approach can be time consuming, and often only an adequate design is found rather than the optimal design for the available resources. Optimal use of resources should be imperative when possible lives are at risk. To demonstrate the practicality of this new structured approach for optimizing a safety system design, a high-integrity safety system has been used. Each design is analysed using the binary decision diagram analysis technique to establish the system unavailability, which is penalized if the system constraints are exceeded. System constraints indicate the limitations on the resources that can be utilized. The SDE approach highlights good and bad settings for possible design variables. This knowledge can then be used by more sophisticated search techniques. The latter part of this paper analyses the results from the best design generated using the SDE, for further optimization using localized optimization approaches.

show abstract

Section: Matrix Experimentsmentioning

confidence: 99%

Using statistically designed experiments for safety system optimization

Andrews

Bartlett

2004

Proceedings of the Institution of Mechanical Engineers, Part E:

View full text Add to dashboard Cite

show abstract

“…Since the cost has no upper bound in this case, the result is always within the solution space albeit it can be suboptimal, hence it is an approximation. [5] had implemented integer programming techniques to solve problems related with systems reliability design. This method is similar to using Lagrange Multipliers, implemented by [6].…”

Section: Target Reliability Approachmentioning

confidence: 99%

Design and analysis of the redundancy allocation problem using a greedy technique

Nanda¹,

Sharma²,

Kundnani³

et al. 2016

IOSR

View full text Add to dashboard Cite

“…Different methods have been developed for solving the RAPs. Exact optimization methods like dynamic programming (Fyffe et al, 1968;Ng & Sancho, 2001), integer programming (Misra & Sharma, 1991), Lagrangean multipliers (Misra, 1972), and various types of the meta-heuristic algorithms such as genetic algorithm (Hamadani & Khorshidi, 2013;Ardakan et al, 2015), ant-colony optimization (Chia & Smith, 2004), Immune algorithm (Chen & You, 2004), the surrogate constraint method (Onishi et al, 2007), variable neighborhood search (VNS) algorithms (Liang & Chen, 2007), Tabu search (TS) algorithm (Ouzineb et al, 2008), and particle swarm optimization (Beji et al, 2010;Wu et al, 2011;Garg, 2013) have been used for maximizing system reliability.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective availability-redundancy allocation problem for a system with repairable and non-repairable components

Zoulfaghari¹,

Hamadani²,

Ardakan³

2015

10.5267/j.dsl

View full text Add to dashboard Cite

Reliability is one of the most important characteristics of the electrical and mechanical systems with applications in the space communication industries, internet networks, telecommunication systems, power generation systems, and productive facilities. What adds to the importance of reliability in these systems are system complications, nature of competitive markets, and increasing production costs due to failures. This paper investigates availability optimization of a system using both repairable and non-repairable components, simultaneously. The availability-redundancy allocation problems involve the determination of component availability (i.e., life time and repair time of the components) and the redundancy levels that produce maximum system availability. These problems are often subject to some constraints on their components such as cost, weight, and volume. To maximize the availability and to minimize the total cost of the system, a new Mixed Integer Nonlinear Programming (MINLP) model is presented. To solve the proposed model, an improved version of the genetic algorithm is designed as an efficient meta-heuristic algorithm. Finally, in order to verify the efficiency of the proposed algorithm, a numerical example of a system is presented that consists of both repairable and non-repairable components.Growing Science Ltd. All rights reserved. 5

show abstract

An efficient algorithm to solve integer-programming problems arising in system-reliability design

Cited by 172 publications

References 19 publications

Using statistically designed experiments for safety system optimization

Using statistically designed experiments for safety system optimization

Design and analysis of the redundancy allocation problem using a greedy technique

Multi-objective availability-redundancy allocation problem for a system with repairable and non-repairable components

Contact Info

Product

Resources

About