A knowledge management system for series-parallel availability optimization and design

Juang, Ying-Shen; Lin, Shui-Shun; Kao, Hsing-Pei

doi:10.1016/j.eswa.2006.08.023

Cited by 83 publications

(46 citation statements)

References 46 publications

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“…Sachdeva et al (2008) described a new multi criteria optimization framework for deriving optimal maintenance schedules for Preventive Maintenance (PM) which considers availability, maintenance cost and life cycle costs as the criteria for optimization using Petri Net. Juang et al (2008) proposed a GA based optimization model to optimize availability of a series-parallel system. Azaron et al (2009) developed a new methodology for reliability evaluation and optimization of nonrepairable dissimilar component cold standby redundant systems.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis and optimization for CSDGB filling system of a beverage plant using particle swarm optimization

Kumar¹,

Tewari²

2017

10.5267/j.ijiec

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The paper deals with the performance analysis and optimization for Carbonated Soft Drink Glass Bottle (CSDGB) filling system of a beverage plant using Particle Swarm Optimization (PSO) approach. The CSDGB system consists of seven main subsystems arranged in series namely Uncaser, Bottle Washer, Electronic Inspection Station, Filling Machine, Crowner, Coding Machine and Case Packer. Considering exponential distribution for probable failures and repairs, mathematical modeling is performed using Markov Approach (MA). The differential equations have been derived on the basis of probabilistic approach using transition diagram. These equations are solved using normalizing condition and recursive method to drive out the steady state availability expression of the system i.e. system's performance criterion. The performance optimization of system has been carried out by varying the number of particles and number of generations. It has been observed that the maximum availability of 90.27% is achieved at flock size of 55 and 90.84% at 300 th generation. Thus, findings of the paper will be useful to the plant management for execution of proper maintenance decisions.

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Section: Introductionmentioning

confidence: 99%

Performance analysis and optimization for CSDGB filling system of a beverage plant using particle swarm optimization

Kumar¹,

Tewari²

2017

10.5267/j.ijiec

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“…In the recent years, researchers made a great effort improving the availability allocation methods. For example, Elegbede and Adjallah applied the genetic algorithms to solve the NP-hard multiobjective optimal availability allocation problem for series-parallel systems [4]; Chiang and Chen proposed a simulated annealing based multiobjective genetic algorithm (saMOGA) to solve the optimal availability allocation problem for series-parallel systems [5]; Barabady 2 Mathematical Problems in Engineering and Kumar used the availability importance measure based on MTBF and MTTR to find optimal allocation results with the minimum cost based on genetic algorithm for series, parallel, and series-parallel systems [6]; Juang et al proposed a genetic algorithm based on a knowledge-based interactive decision support system to improve the availability allocation for series-parallel systems [7]; Liu studied the availability optimization problem for -stage standby system under different resource and design configuration constraints by applying Tabu-genetic algorithm combination method [8]; Xie et al extended the optimal availability allocation to consider redundancy allocation and spare parts provisioning simultaneously for -out-of-: G systems [9]. However, the systems mentioned above are simple structured ones.…”

Section: Introductionmentioning

confidence: 99%

Availability Allocation of Networked Systems Using Markov Model and Heuristics Algorithm

Liu²,

Huang³

2014

Mathematical Problems in Engineering

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It is a common practice to allocate the system availability goal to reliability and maintainability goals of components in the early design phase. However, the networked system availability is difficult to be allocated due to its complex topology and multiple down states. To solve these problems, a practical availability allocation method is proposed. Network reliability algebraic methods are used to derive the availability expression of the networked topology on the system level, and Markov model is introduced to determine that on the component level. A heuristic algorithm is proposed to obtain the reliability and maintainability allocation values of components. The principles applied in the AGREE reliability allocation method, proposed by the Advisory Group on Reliability of Electronic Equipment, and failure rate-based maintainability allocation method persist in our allocation method. A series system is used to verify the new algorithm, and the result shows that the allocation based on the heuristic algorithm is quite accurate compared to the traditional one. Moreover, our case study of a signaling system number 7 shows that the proposed allocation method is quite efficient for networked systems.

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“…Wang [1] defines availability indicating the probability that repairable systems maintain the function at a specific moment. It is also noted that system availability is a concept closely related to reliability and refers to the scale of measuring the reliability of repairable system [2]. Since availability comprises both reliability and maintainability parameters, it can evaluate the effectiveness of repairable systems more precisely than reliability.…”

Section: Introductionmentioning

confidence: 99%

“…But, few literature have been published for this kind of availability allocation problem ( [2]- [8]). Most of them formulated the problem of availability allocation as a multi-objective optimization problem to maximize the system availability and minimize the total system cost ( [2]- [5])…”

Section: Introductionmentioning

confidence: 99%

“…Observing these literatures, various Genetic algorithms are applied to find the optimal number of redundant components for reliability optimization problems [14] or to obtain the optimal number of components, the optimal failure rate and repair rate of each component in each subsystem simultaneously for availability optimization problems ( [2], [4], [5], [19], [20]). As a member of meta-heuristic algorithm, Genetic Algorithm has proved itself to be widely accepted by many scholars in the field of reliability and availability optimization to search for the optimal solution based on various discrete and continuous decision variables.…”

Section: Introductionmentioning

confidence: 99%

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Availability Optimization for Repairable n-Stage Standby System by Applying Tabu-GA Combination Method

Liu¹

2013

IJMO

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Abstract-This study aims at the availability optimization problem for n-stage standby system under different resource and design configuration constraints by applying Tabu-GA combination method. From the point of view of logistics engineering, availability optimization applied in the initial system development period, plays a key role to affect system reliability, system maintenance planning, logistics requirements, and related costs during system planned life cycle. In general, system availability can be improved by increasing component reliability level, component repair rate or the number of components in each subsystem. This proposed model combines Tabu method with Genetic Algorithm to solve system availability optimization problem. Through our method applied in the initial phase of system design and development, we can find the optimal allocation of component redundancy number, reliability and maintainability levels to minimize the total system cost under different configuration constraints such as weight, volume, and system availability requirements. The proposed numerical examples are demonstrated based on different system constraint requirements and parameter values. Through a simulation study of 30 times of calculations by applying this proposed combination method, stable results are clearly showed. Consequently, all numerical results and simulation studies clearly show that the proposed availability optimization model and Tabu-GA combination method developed in this paper can help solving system availability optimization problem in the initial system design and development period.

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A knowledge management system for series-parallel availability optimization and design

Cited by 83 publications

References 46 publications

Performance analysis and optimization for CSDGB filling system of a beverage plant using particle swarm optimization

Performance analysis and optimization for CSDGB filling system of a beverage plant using particle swarm optimization

Availability Allocation of Networked Systems Using Markov Model and Heuristics Algorithm

Availability Optimization for Repairable n-Stage Standby System by Applying Tabu-GA Combination Method

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