Maintenance Optimization using Combined Fuzzy Genetic Algorithm and Local Search

Maatouk, Imane; Chebbo, Nazir; Jarkass, Iman; Châtelet, Éric

doi:10.1016/j.ifacol.2016.07.865

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Hennequin and Restrepo [22] raised the issue of uncertainty and fuzziness brought about by humans in system. This impression has long been recognised by scholars, who without even treating the subject of sustainability have incorporated it into their maintenance models [27,33].…”

Section: Maintenance and Sustainability Issuesmentioning

confidence: 99%

“…The approach consists of a failure-modes-and-effects-analysis-fuzzy based tool, a miscellaneous seven-dimensioned method and a strategy selection tool based on inputs from the previous two stages. Maatouk et al [33] hybridised fuzzy logic-directed genetic algorithm as well as local search in the solution of a maintenance problem. They developed an optimisation framework that consists of many state series-parallel units in order to minimise overall cost.…”

Section: Maintenance and Sustainability Issuesmentioning

confidence: 99%

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A multi-hierarchical framework for ranking maintenance sustainability strategies using PROMETHEE and fuzzy entropy methods

Ighravwe

Oke

2017

J Build Rehabil

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Today, maintenance plays a significant responsibility in all stages of equipment life due to strong government interests in environmentally conscious manufacturing. The need of the hour is to ignore the sparse attention to sustainable maintenance research and pursue valuable links between maintenance strategy and sustainable maintenance. In maintenance strategy choice, available reports have not sufficiently addressed the imbalance caused by uncertainties in maintenance practices. In addition, current reports on maintenance strategy sustainability focus on technical and economic aspects of maintenance and scantily treat environmental, social and safety criteria. This affects the quality of decisions in maintenance systems. To remedy this situation, this study applies fuzzy entropy weight and PROMETHEE (Preference Ranking Organisation Method for Enrichment Evaluation) in ranking maintenance sustainability strategies. The proposed approach is tested in a cement plant. Based on the choice criteria, the PROMETHEE methods results identified the best maintenance strategy as maintenance optimisation strategy. Workforce training strategy was identified as the worst maintenance sustainability strategy. These obtained results were compared with fuzzy TOPSIS (Technique of Preference Order by Similarity to Ideal Solution) approach and the practical application of the approach was verified. The results serve as a basis and a platform for further application of the approach in other manufacturing companies.

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Section: Maintenance and Sustainability Issuesmentioning

confidence: 99%

Section: Maintenance and Sustainability Issuesmentioning

confidence: 99%

A multi-hierarchical framework for ranking maintenance sustainability strategies using PROMETHEE and fuzzy entropy methods

Ighravwe

Oke

2017

J Build Rehabil

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“…Momeni et al (2011) also studied multi-criteria decision making for selection of a maintenance strategy using fuzzy TOPSIS-based approach. Maatouk et al (2016) used a fuzzy logic controlled genetic algorithm to solve a preventive maintenance (PM) optimisation problem. Mazurkiewicz (2015) used fuzzy logic-based expert system to improve the diagnostics of a conveyor belt system.…”

Section: Introductionmentioning

confidence: 99%

Measuring maintenance activity effectiveness

Rana

Koroitamana

2018

JQME

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Purpose The purpose of this paper is to provide a framework for measuring the imprecise and subjective “effectiveness” of a major maintenance activity. Such a measure will not only bring objectivity in gauging the effectiveness of maintenance task carried out by the workforce without any intervention from an expert but also help in measuring the slow degradation of the performance of the concerned major equipment/system. Design/methodology/approach The paper follows a three-step approach. First, identify a set of parameters considered important for estimating the maintenance activity effectiveness. Second, generate a set of data using expert opinions on a fuzzy performance measure of maintenance activity effectiveness (output). Also, find an aggregated estimate of the effectiveness by analysing the consensus among experts. This requires using a part of the “fuzzy multiple attribute decision making” process. Finally, train a neuro-fuzzy inference system based on input parameters and generated output data. Findings The paper analysed major maintenance activity carried out on diesel engines of a power plant company. Expert opinions were used in selection of key parameters and generation of output (effectiveness measure). The result of a trained adaptive neuro-fuzzy inference system (ANFIS) matched acceptably well with that aggregated through the expert opinions. Research limitations/implications In view of unavailability of data, the method relies on training a neuro-fuzzy system on data generated through expert opinion. The data as such are vague and imprecise leading to lack of consensus between experts. This can lead to some amount of error in the output generated through ANFIS. Originality/value The originality of the paper lies in presentation of a method to estimate the effectiveness of a maintenance activity.

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Development of an intelligent decision making tool for maintenance planning using fuzzy logic and dynamic scheduling

Andrew

Kumanan

2019

Int. j. inf. tecnol.

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Maintenance Optimization using Combined Fuzzy Genetic Algorithm and Local Search

Cited by 5 publications

References 5 publications

A multi-hierarchical framework for ranking maintenance sustainability strategies using PROMETHEE and fuzzy entropy methods

A multi-hierarchical framework for ranking maintenance sustainability strategies using PROMETHEE and fuzzy entropy methods

Measuring maintenance activity effectiveness

Development of an intelligent decision making tool for maintenance planning using fuzzy logic and dynamic scheduling

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