Multi-objective design optimisation of repairable<i>k</i>-out-of-<i>n</i>subsystems in series with redundant dependency

Habib, Milia; Yalaoui, Farouk; Chehade, Hicham; Jarkass, Iman; Chebbo, Nazir

doi:10.1080/00207543.2017.1346319

Cited by 9 publications

(7 citation statements)

References 44 publications

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“…The problems categorized as allocation problems are resource allocation problem (RAP) [66], [67], [68], [69], [70], [71], [72], redundancy allocation problem (denoted as ReAP) [73], [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], reliability redundancy allocation problem [86], buffer allocation [87], order allocation planning [88], land allocation [89], allocation of D-STATCOM in DSs [90], channel allocation in mobile computing [91], agile team allocation problem [92] and continuous berth allocation problem [93].…”

Section: A Nsga-ii For Mocopsmentioning

confidence: 99%

“…Habib et al [73] proposed design optimization of repairable k-out-of-n subsystems using NSGA-II with two different constrained handling techniques, penalty method and constraint domination principle (CDP). These two approaches were compared with the SPEA-II algorithm, and also the full enumeration method (FEM) was used for obtaining the true Pareto front.…”

Section: B) Allocation Problemmentioning

confidence: 99%

“…The most used performance metrics in the studied literature are given in Table X. The other rarely used performance metrics are maximum sum of the objective-values (MaxSum) [102], [99], maximum distance Dmax [175], [157], epsilon indicator [45], [134], average quality [157], [175], average hypervolume [105], [121], width measure (M2 metric) [147], system performance [148], solution quality [54], size of reference set [62], relative percentage difference between objective-values [210], relative and absolute quality [59], overall non-dominated vector generation (ONVG) [175], number of unscheduled tasks [163], number of generations [68], number of channel reuses, user level fairness, and stability of the communication mode of the D2D users [96], norm based pure diversity metric [72], modified mean ideal distance measure [127], maximum Pareto front error [201], M1,M2,M3 [110], k-distance [198], hole-relative size metric, µ distance metric [73], error, ratio, distance based measure, fairness [158], empirical attainment function [45], data envelopment analysis [83], data dependency threshold [68], convex hull of the approximated efficient frontier [59], convergent metric [140], capacity measure [72], average rank index, average crowding distance and the mapping pattern of solutions [89], convergent rate metric [85], infeasibility metric [85]…”

Section: ) Performance Measuresmentioning

confidence: 99%

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A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

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This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

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Section: A Nsga-ii For Mocopsmentioning

confidence: 99%

Section: B) Allocation Problemmentioning

confidence: 99%

Section: ) Performance Measuresmentioning

confidence: 99%

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A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

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“…A graphical parameter modeling these interactions defines the level of dependence. 20,26 Four main cases (classes of dependence) can be considered as follows. 17 Case 1: independence…”

Section: Problem Descriptionmentioning

confidence: 99%

An adaptive cuckoo optimization algorithm for system design optimization under failure dependencies

Mellal

Zio

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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This article presents an algorithm for optimal redundancy and repair team allocation with respect to minimum system cost and a system availability constraint. Four scenarios are considered for the failures occurring in the subsystems of the system: independence, linear dependence, weak dependence, and strong dependence. An adaptive cuckoo optimization algorithm is developed for solving the nonlinear integer optimization problem of allocation. A series–parallel system with six subsystems is considered as a case study for demonstration purposes. The results obtained highlight the good performance of the developed algorithm.

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“…The multi-component maintenance decision model has some shortcomings in the management of complex large production system maintenance events. Most studies focus on multi-component maintenance decisions by assuming that equipment is a whole component or series of connected components [ 25 , 26 ]. However, the actual equipment is a mixed combination of complex production systems including many maintenance events.…”

Section: Introductionmentioning

confidence: 99%

A multi-event combination maintenance model based on event correlation

Guo¹,

Lyu²,

Chen³

et al. 2018

PLoS ONE

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Due to the complexity of large production systems, maintenance events are diverse, simultaneous and dynamic. Appropriate maintenance management of complex large production systems can guarantee high availability and save maintenance costs. However, current maintenance decision-making methods mainly focus on the maintenance events of single-components and series connection multi-components; little research pays attention to the combination maintenance of different maintenance events. Therefore, this paper proposes a multi-event combination maintenance model based on event correlation. First, the maintenance downtime and cost of three types of maintenance events under different maintenance beginning times and degrees are analysed. Then, shared maintenance downtime and cost models are established by maintenance event correlations. In addition, a multi-event combination maintenance model is constructed to achieve the goal of the highest availability and the lowest cost rate in both the decision-making cycle and the remaining life. Moreover, a particle swarm optimization algorithm based on interval segmentation for model solving is designed. Finally, a numerical example is presented to illustrate the model.

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Multi-objective design optimisation of repairablek-out-of-nsubsystems in series with redundant dependency

Cited by 9 publications

References 44 publications

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

An adaptive cuckoo optimization algorithm for system design optimization under failure dependencies

A multi-event combination maintenance model based on event correlation

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