Tabu search for the redundancy allocation problem of homogenous series–parallel multi-state systems

Ouzineb, Mohamed; Nourelfath, Mustapha; Gendreau, Michel

doi:10.1016/j.ress.2007.06.004

Cited by 145 publications

(65 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ant [7] and bee [18] colony optimization techniques can also be used to solve this problem. Artificial immune system algorithms, [9] improved surrogate constraint methods [10] and Tabu search [16] have been successfully implemented as well. [21] have taken into account, the variability data of reliability of components, gathered through field tests.…”

Section: = =mentioning

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

Section: = =mentioning

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

“…In many situations the evaluation of such a big set of solutions is not feasible in reasonable time so the optimization might require hours or even days. A tabu search (TS) heuristic has been used in [32] to derive component allocation under availability constraints in the context of embedded systems. Finally, Frey et al [33] proposed a combined metaheuristic-simulation approach based on a genetic algorithm to derive deployment architecture and runtime reconfiguration rules while moving a legacy application to the Cloud environment.…”

Section: Related Workmentioning

confidence: 99%

A Model-Driven DevOps Framework for QoS-Aware Cloud Applications

Guerriero

Ciavotta

Gibilisco

et al. 2015

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

View full text Add to dashboard Cite

Recently we witnessed a deep transformation in the the design, development and management of modern applications, which have grown in scope and size becoming distributed and service-oriented. A big part in this metamorphosis is played by the Cloud with the availability of almost-infinite resources, high availability and outsourced maintenance. This has led to the emergence of new software development methodologies to effectively deal with this paradigm shift in the field of software engineering. DevOps is one of them, it advocates for a greater level of collaboration and convergence between developers and other IT professionals. Consequently, new tools, purposely designed to ease this process, are required. In this scenario, we present SPACE4Cloud, a DevOps integrated environment for model-driven design-time quality of service assessment and optimization, and runtime capacity allocation of Cloud applications.

show abstract

“…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

Tabu search for the redundancy allocation problem of homogenous series–parallel multi-state systems

Cited by 145 publications

References 24 publications

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

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

A Model-Driven DevOps Framework for QoS-Aware Cloud Applications

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

Contact Info

Product

Resources

About