Industrial plant management, especially maintenance optimisation, is usually characterised by the need to consider multiple noncommensurable and often conflicting objectives. Equipment can be over-maintained, increasing PM (preventive maintenance) expenditures, or under-maintained (too low CM (corrective maintenance), increasing catastrophic failures. Thus it is very difficult to determine the optimal maintenance strategy that maximises the profitability of the studied equipment considering different criteria. This article provides a solution for the joint optimisation of PM strategies applied on several pieces of equipment, i.e. a simplified system consisting of three identical plastic injection machines and a painting station producing plastic hubcaps for car manufacturers, including injection, storage, transport, and painting. The research is focused on the problem of PM optimisation in a manufacturing environment with the objective of determining the optimal PM frequencies for multi-equipment systems under cost and profit criteria. The approach developed takes into account the interaction of production, work-in-process material, quality and maintenance. For this purpose, a model that considers maintenance, productive speed loss and nonquality costs along with productive profit has been developed, implemented using DES (discrete event simulation), and optimised using the NSGA-II (nondominated sorting genetic algorithm) MOEA (multi-objective evolutionary algorithm). Thus, the suitability of DES to model or modify complex system models is combined with the aptitude that MOEAs have shown to deal with MOPs (multi-objective optimisation problems). Each machine of the model consists of three subsystems modelled as components in serial configuration. The developed MOP offers solutions situated in a Pareto optimal front, and thus the analyst can select externally the best maintenance strategy based on Pareto set scores in cost and profit criteria.


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    Title :

    Optimizing preventive maintenance by combining discrete event simulation and genetic algorithms


    Contributors:

    Published in:

    Hydrocarbon Processing ; 86 , 10 ; 115-122


    Publication date :

    2007


    Size :

    6 Seiten, 6 Bilder, 7 Tabellen, 15 Quellen



    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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