Decision-making method of optimum inspection interval for plant maintenance by genetic algorithms

Yamamoto, Mikio; Zaier, Riadh; Chen, Peng; Toyota, Toshio

doi:10.1109/ecodim.2001.992400

Cited by 5 publications

(3 citation statements)

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“…The time horizon was considered 52 weeks (1 year), it means, short-term, and four types of maintenance were evaluated. The monitoring task of power generation plants consists of a decision-making tool [24,1]. The result, as several optimization problems and reported in [14], was a number of efficient solutions which we considered the candidates of final decision making problem.…”

Section: Case Studymentioning

confidence: 99%

Fuzzy Multi-Criteria Decision Making Methods with Uncertainty Scenarios

Alves¹,

Guimarães²

2018

Proceedings XIII Brazilian Congress on Computational Inteligence

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Fuzzy Multi-criteria decision making methods have been provided to help the decision-makers in their complex decisions about future uncertainties. Taking into consideration uncertainties such as vagueness and future scenarios, this paper aims to apply the methods Fuzzy-MultiMoora, Fuzzy-Topsis Linear, Fuzzy-Topsis Vector, Fuzzy-Vikor and Fuzzy-Waspas in a Hydrothermal Dispatch problem. Five scenarios were evaluated by varying hydrology and energy demand parameters, from very pessimistic to very optimistic. Two decision makers made explicit their preferences weighting three criteria: Cost, Rationing and Distance. The normalized fuzzy numbers were calculated using the concept of alpha-cuts. Finally, the indexes were aggregated into a final ordering considering weights for the methods based on the Kendall tau distance. The best solutions were compared in relation to the criteria. It was observed that these solutions presented good results in all scenarios evaluated.

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Section: Case Studymentioning

confidence: 99%

Fuzzy Multi-Criteria Decision Making Methods with Uncertainty Scenarios

Alves¹,

Guimarães²

2018

Proceedings XIII Brazilian Congress on Computational Inteligence

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“…Eqs. (2)(3)(4)(5)(6)(7)(8) bear the integer part of the problem, those are constraints related to the GMS part, while Eqs. (9-16) embody the continuous part, so the HTD.…”

Section: Inmentioning

confidence: 99%

“…We focus on those works that have applied metaheuristic techniques based on either pure genetic algorithms or combinations of them with other metaheuristics. In the first place, we list some relevant works that offer solutions for GMS based solely on GA [1][2][3][4][5][6][7], or improved versions of them [8,9]. Secondly, we pay special attention to those named hybrid algorithms, understanding by that, combinations of techniques to solve the problem under scope.…”

Section: Introductionmentioning

confidence: 99%

Maintenance of generation units coordinated with annual hydrothermal scheduling using a hybrid technique

Ramírez-Martínez

Martínez-Sánchez

Escobar-Zuluaga

et al. 2017

Rev. Fac. Ing. Univ. Antioquia

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This paper presents a hybrid technique for solving the generation maintenance scheduling coordinated with middle term hydrothermal dispatch based on the Chu-Beasley genetic algorithm and linear programming. It takes into consideration the non-linearity derived from the cost of the fuel for thermal power plants. The output of the genetic algorithm is a proposal for the start week of the generation maintenance plan that minimises the hydrothermal dispatch cost. This work has two main contributions: on one hand proposing a solution that integrates hydrothermal dispatch and maintenance scheduling in a single model, therefore a new mathematical model and, on the other hand the proposed solution applies a specialised genetic algorithm that has not been applied before to solve this problem. The test system to validate the methodology is composed of three hydro plants and two thermal plants split into 22 generation units, considering three types of preventive maintenance, in a planning term of one year (52 weeks). This methodology combines an exact technique and a specialised genetic algorithm, which favours convergence. RESUMEN:Este artículo presenta una técnica híbrida para resolver la programación del mantenimiento de las unidades de generación coordinado con el despacho hidrotérmico de mediano plazo. La solución se basa en el algoritmo genético de Chu-Beasley y en la técnica de programación lineal. Tiene en cuenta no linealidades derivadas del costo de los combustibles utilizados por las centrales térmicas. La salida del algoritmo genético es una propuesta de la semana de inicio del plan de mantenimiento de cada unidad de generación que minimiza el costo del despacho hidrotérmico. Las dos principales contribuciones de este trabajo son que propone un modelo matemático que coordina dos problemas que en la literatura se han resuelto de forma separada, y que aplica un algoritmo genético especializado que aún no ha sido utilizado para resolver el problema coordinado. El sistema de prueba para validar la metodología se compone de tres centrales hidroeléctricas y dos centrales térmicas dividas en 22 unidades de generación, teniendo en cuenta el mantenimiento preventivo, en un horizonte de planeamiento de un año (52 semanas). Esta metodología combina una técnica exacta con un algoritmo genético especializado, lo que favorece la convergencia.18

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