Maintenance optimization of reconfigurable systems based on multi-objective Birnbaum importance

2020 ◽  
pp. 1748006X2090198
Author(s):  
Chenyang Ma ◽  
Wei Wang ◽  
Zhiqiang Cai ◽  
Jiangbin Zhao
Keyword(s):  
Genetic Algorithm ◽  
System Reliability ◽  
System Performance ◽  
Cost Effective ◽  
System Structure ◽  
Maintenance Cost ◽  
Reconfigurable Systems ◽  
Maintenance Optimization ◽  
Multi Objective ◽  
Better Than

Reconfigurable systems can meet the changing requirements of system performance by several approaches, such as adjusting the system structure, improving the component performance, and reassigning components. However, it is also challengeable to find a cost-effective maintenance scheme by integrating these maintenance approaches. This article investigates the multi-objective maintenance optimization problem for reconfigurable systems with the consideration of maintenance cost and system reliability. First, the multi-objective maintenance optimization model is established to maximize the system reliability and minimize the total maintenance cost considering the constraints on budget and system performance. Second, a multi-objective Birnbaum importance is proposed to quantify the contribution of the individual component to the system reliability. The multi-objective Birnbaum importance–based non-dominated sorting genetic algorithm II is developed to obtain the optimal maintenance scheme with the maximum system reliability and minimum maintenance cost. Finally, the performance of multi-objective Birnbaum importance–based non-dominated sorting genetic algorithm II is proved by three numerical experiments. Experiment 1 verifies the advantage of multi-objective Birnbaum importance compared with Birnbaum importance to improve the system reliability in direct maintenance. Experiment 2 shows that the effectiveness of multi-objective Birnbaum importance is much better than that of the Birnbaum importance to enhance the performance of non-dominated sorting genetic algorithm II in comprehensive maintenance. Experiment 3 illustrates that the performance of multi-objective Birnbaum importance–based non-dominated sorting genetic algorithm II is better than that of other multi-objective algorithms combining with multi-objective Birnbaum importance.

scholarly journals Maintenance Optimization for Repairable Deteriorating Systems under Imperfect Preventive Maintenance

Mathematics ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 716
Author(s):  
Juhyun Lee ◽  
Byunghoon Kim ◽  
Suneung Ahn
Keyword(s):  
System Reliability ◽  
Preventive Maintenance ◽  
Cost Effective ◽  
Optimal Number ◽  
Sensitivity Analyses ◽  
Maintenance Cost ◽  
Maintenance Optimization ◽  
Maintenance Strategies ◽  
Cost Rate ◽  
Maintenance Activities

This study deals with the preventive maintenance optimization problem based on a reliability threshold. The conditional reliability threshold is used instead of the system reliability threshold. Then, the difference between the two thresholds is discussed. The hybrid failure rate model is employed to represent the effect of imperfect preventive maintenance activities. Two maintenance strategies are proposed under two types of reliability constraints. These constraints are set to consider the cost-effective maintenance strategy and to evaluate the balancing point between the expected total maintenance cost rate and the system reliability. The objective of the proposed maintenance strategies is to determine the optimal conditional reliability threshold together with the optimal number of preventive maintenance activities that minimize the expected total maintenance cost per unit time. The optimality conditions of the proposed maintenance strategies are also investigated and shown via four propositions. A numerical example is provided to illustrate the proposed preventive maintenance strategies. Some sensitivity analyses are also conducted to investigate how the parameters of the proposed model affect the optimality of preventive maintenance strategies.

Research on the Multi-Objective Optimization Model of System-Level BIT Testability Index Determination

2011 ◽  
Vol 121-126 ◽  
pp. 2223-2227 ◽  
Author(s):  
Chun Sheng Zhu ◽  
Qi Zhang ◽  
Fan Tun Su ◽  
Hong Liang Ran
Keyword(s):  
Genetic Algorithm ◽  
Life Cycle ◽  
Mathematical Models ◽  
Optimization Model ◽  
System Reliability ◽  
Life Cycle Cost ◽  
System Level ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Life Circle

By weighing reliability, maintainability, availability and life-cycle cost of equipment which are influenced by testability，the testability indexes of system level BIT are determined on the basis of maximum system reliability & maintainability and minimum the life-circle cost. The influence mathematical models of system reliability, maintainability, availability and life-circle cost are established. According to these mathematical models, the multi-objective optimization model of system-level BIT testability indexes is established. The multi-objective optimization model is solved using Non-dominated Sorting Genetic Algorithm II, and the validity of the multi-objective optimization model is proved through an example.

A Software Reliability Model for Cloud-Based Software Rejuvenation Using Dynamic Fault Trees

2015 ◽  
Vol 25 (09n10) ◽  
pp. 1491-1513 ◽  
Author(s):  
Jean Rahme ◽  
Haiping Xu
Keyword(s):  
System Reliability ◽  
System Performance ◽  
High Reliability ◽  
Cost Effective ◽  
Reliability Function ◽  
Cloud Services ◽  
Software Faults ◽  
Software Aging ◽  
Software Rejuvenation ◽  
Dynamic Fault Tree

Correctly measuring the reliability and availability of a cloud-based system is critical for evaluating its system performance. Due to the promised high reliability of physical facilities provided for cloud services, software faults have become one of the major factors for the failures of cloud-based systems. In this paper, we focus on the software aging phenomenon where system performance may be progressively degraded due to exhaustion of system resources, fragmentation and accumulation of errors. We use a proactive technique, called software rejuvenation, to counteract the software aging problem. The dynamic fault tree (DFT) formalism is adopted to model the system reliability before and during a software rejuvenation process in an aging cloud-based system. A novel analytical approach is presented to derive the reliability function of a cloud-based Hot SPare (HSP) gate, which is further verified using Continuous Time Markov Chains (CTMC) for its correctness. We use a case study of a cloud-based system to illustrate the validity of our approach. Based on the reliability analytical results, we show how cost-effective software rejuvenation schedules can be created to keep the system reliability consistently staying above a predefined critical level.

Grid Task Scheduling Algorithm Based on Non-Conflict Degree

2012 ◽  
Vol 239-240 ◽  
pp. 1497-1500
Author(s):  
Fu Qiao ◽  
Yan Juan Zhang ◽  
Ze Yuan Gu ◽  
Juan Wang ◽  
Guo Yin Zhang
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Solution Procedure ◽  
Multi Objective ◽  
Task Scheduling Algorithm ◽  
Conflict Problem ◽  
Tasks Scheduling ◽  
Better Than

To solve the multi-objective conflict problem of independent task scheduling in grid computing, model the system along the direction of multi-objective gradient, give the model solution procedure, propose grid scheduling algorithm based on multi-objective non-conflict degree, that is, application of gradient ascent method does a quadratic optimization for the global optimal solution obtained by genetic algorithm, compensate for the GA (genetic algorithm) deficiency in the ability of local search. The experiments demonstrate that the proposed algorithm is better than other tasks scheduling algorithms simply using GA.

scholarly journals MULTI-OBJECTIVE LAND USE OPTIMIZATION THROUGH PARALLEL PARTICLE SWARM ALGORITHM: CASE STUDY BABOLDASHT DISTRICT OF ISFAHAN, IRAN

2016 ◽  
Vol 10 (1) ◽  
pp. 42-49
Author(s):  
Alireza Sahebgharani
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Planning ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Multi Objective ◽  
Land Use Optimization ◽  
Land Types ◽  
Better Than

Land use planning seeks to divide land, the most valuable resource in the hands of planners, among different land types. During this process, various conflicting objectives are emerged which land use planners should prepare land use plans satisfying these objectives and deal with a large set of data and variable. For this reason, land use allocation is a multi-objective NP-hard optimization problem which is not solvable by the current exact methods. Therefore, solving land use optimization problem relies on the application of meta-heuristics. In this paper, a novel meta-heuristic named parallel particle swarm is developed to allocate seven land types (residential, commercial, cultural, educational, medical, sportive and green space) to Baboldasht district of Isfahan covered by 200 allocation cells with size 1000 m2 for maximizing compactness, compatibility and suitability objective functions. Afterwards, the outputs of the new developed algorithm are compared to the outputs of genetic algorithm. The results demonstrated that the parallel particle swarm is better than genetic algorithm in terms of both solution quality (1.35%) and algorithm efficiency (63.7%). The results also showed that the outputs achieved by both algorithms are better than the current state of land use distribution. Thus, the method represented in this paper can be used as a useful tool in the hands of urban planners and decision makers, and supports the land use planning process.

Multi-objective imperfect selective maintenance optimization for series-parallel systems with stochastic mission duration

2022 ◽  
pp. 1748006X2110666
Author(s):  
Chun Su ◽  
Kui Huang ◽  
Zejun Wen
Keyword(s):  
Optimization Model ◽  
Hazard Rate ◽  
Random Variable ◽  
Maintenance Cost ◽  
Maintenance Optimization ◽  
Engineering System ◽  
Nsga Ii ◽  
Multi Objective ◽  
Mission Duration ◽  
Selective Maintenance

To improve the probability that an engineering system successfully completes its next mission, it is crucial to implement timely maintenance activities, especially when maintenance time or maintenance resources are limited. Taking series-parallel system as the object of study, this paper develops a multi-objective imperfect selective maintenance optimization model. Among it, during the scheduled breaks, potential maintenance actions are implemented for the components, ranging from minimal repair to replacement. Considering that the level of maintenance actions is closely related to the maintenance cost, age reduction coefficient and hazard rate adjustment coefficient are taken into account. Moreover, improved hybrid hazard rate approach is adopted to describe the reliability improvement of the components, and the mission duration is regarded as a random variable. On this basis, a nonlinear stochastic optimization model is established with dual objectives to minimize the total maintenance cost and maximize the system reliability concurrently. The fast elitist non-dominated sorting genetic algorithm (NSGA-II) is adopted to solve the model. Numerical experiments are conducted to verify the effectiveness of the proposed approach. The results indicate that the proposed model can obtain better scheduling schemes for the maintenance resources, and more flexible maintenance plans are gained.

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