Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem

Lee, Loo Hay; Chew, Ek Peng; Teng, Shaohua; Chen, Yankai

doi:10.1016/j.ejor.2007.05.036

Cited by 100 publications

(52 citation statements)

References 32 publications

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“…Therefore, we can obtain a solution set rather than only one solution using GA. Secondly, multi-objective genetic algorithm (MOGA) (see Deb et al, 2002) based on GA is quite suitable to solve NPHard multi-objective optimization problems. The existing research have shown that MOGA is effective in solving multi-objective problems in many fields (Mansouri, 2005;Tan et al, 2006;Lee et al, 2007Lee et al, , 2008Jozefowiez et al, 2009). Additionally, we improve the NSGA-II to enhance its effectiveness and efficiency.…”

Section: Improved Nondominated Sorting Genetic Algorithm II (Insga-ii)mentioning

confidence: 99%

A method for member selection of cross-functional teams using the individual and collaborative performances

Feng

Jiang

Fan

et al. 2010

European Journal of Operational Research

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a b s t r a c tThe member selection problem is an important aspect of the formation of cross-functional teams (CFTs). Selecting suitable members from a set of candidates will facilitate the successful task accomplishment. In the existing studies of member selection, the individual performance concerning a single candidate is mostly used, whereas the collaborative performance associating with a pair of candidates is overlooked. In this paper, as a solution to this problem, we propose a method for member selection of CFTs, where both the individual performance of candidates and the collaborative performance between candidates are considered. In order to select the desired members, firstly, a multi-objective 0-1 programming model is built using the individual and collaborative performances, which is an NP-hard problem. To solve the model, we develop an improved nondominated sorting genetic algorithm II (INSGA-II). Furthermore, a real example is employed to illustrate the suitability of the proposed method. Additionally, extensive computational experiments to compare INSGA-II with the nondominated sorting genetic algorithm II (NSGA-II) are conducted and much better performance of INSGA-II is observed.

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Section: Improved Nondominated Sorting Genetic Algorithm II (Insga-ii)mentioning

confidence: 99%

A method for member selection of cross-functional teams using the individual and collaborative performances

Feng

Jiang

Fan

et al. 2010

European Journal of Operational Research

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“…Tal fato decorre da alta variabilidade, da não linearidade dessa classe de problemas e da possibilidade de ocorrência de múltiplos objetivos. Nesse sentido, a Simulação a Eventos Discretos (SED) tem sido uma ferramenta útil para análise de desempenho desses sistemas (LEE et al, 2008).…”

Section: Introductionunclassified

Otimização via simulação por metamodelagem: um estudo em casos da área médica

Miranda

Montevechi

Pereira

et al. 2016

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

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Resumo: O uso combinado da simulação a eventos discretos e otimização vem crescendo nos últi-mos anos. No entanto, esta forma de otimização ainda é pouco utilizada, devido principalmente a seu elevado custo computacional. Nesse sentido, vários trabalhos visam desenvolver novos métodos de otimização, buscando acelerar este procedimento. Este trabalho faz uso da técnica de metamodelagem, para criar um modelo matemático, que represente uma determinada saída de um modelo de simulação, e proceder com sua otimização sem a necessidade da utilização do modelo simulado. Para exemplificar o método empregado foram utilizados modelos de simulação ligados à área médi-ca. O uso da metamodelagem permitiu otimizar os modelos de simulação, alcançando soluções estatisticamente iguais as encontradas por um otimizador comercial, com a vantagem de reduzir o tempo computacional do processo em mais de 68% em média. Desta forma, a metamodelagem se apresenta como uma potencial técnica de otimização para a simulação, ao prover soluções de elevada qualidade em um tempo computacional reduzido, quando comparado a métodos de otimização via simulação tradicionais. Palavras-chaves:Otimização via Simulação. Simulação a Eventos Discretos. Metamodelagem. Otimização. Área Médica. Abstract:The combination of discrete event simulation and optimization techniques is growing in recent years. However, such a combination isn't widely used due mainly to high computational costs. Due to this difficulty, some studies are focused on developing more computationally efficient optimization methods. This article uses a metamodeling technique to create a mathematical model for an output of interest, by running a limited number of scenarios of a simulation model and then proceed to optimization of this output model. We argue that this approach results in significantly reduced computational burden compared to optimizing directly on scenario-specific output values obtained from the simulation model. We illustrate this method using simulation models of distinct functional areas across hospitals in Brazil. The use of metamodeling allows optimization of the simulation models, reaching better solutions than those obtained by a commercial optimizer, with the additional advantage of reducing the computational time by more than 68% on average.

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“…The framework proposed by Lee et al integrates simulation, multi-objective computing budget allocation and multi-objective evolutionary algorithms. The computational results show that, for the aircraft spare parts allocation problem, the framework is capable of finding non-dominated inventory and replacement policies with low average costs and high service levels [25]. Rasuo and Duknic report on a research, based on data, collected at general maintenance of military aircraft with the goal of reducing costs and shortening the overhaul cycle.…”

Section: Aircraft Maintenancementioning

confidence: 99%

Aircraft fault forecasting at maintenance service on the basis of historic data and aircraft parameters

Pogačnik¹,

Duhovnik²,

Tavčar³

2017

EiN

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AircrAft fAult forecAsting At mAintenAnce service on the bAsis of historic dAtA And AircrAft pArAmeters prognozowAnie uszkodzeń stAtków powietrznych dlA celów obsługi konserwAcyjnej nA podstAwie ich pArAmetrów orAz dAnych z eksploAtAcji PogAčnik B, Duhovnik J, TAvčAr J. Aircraft fault forecasting at maintenance service on the basis of historic data and aircraft parameters. Eksploatacja i niezawodnosc -Maintenance and reliability 2017; 19 (4): 624-633, http://dx

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Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem

Cited by 100 publications

References 32 publications

A method for member selection of cross-functional teams using the individual and collaborative performances

A method for member selection of cross-functional teams using the individual and collaborative performances

Otimização via simulação por metamodelagem: um estudo em casos da área médica

Aircraft fault forecasting at maintenance service on the basis of historic data and aircraft parameters

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