Optimizing the preventive maintenance scheduling by genetic algorithm based on cost and reliability in National Iranian Drilling Company

Javanmard, Habibollah; Koraeizadeh, Abd al-Wahhab

doi:10.1007/s40092-016-0155-9

Cited by 22 publications

(10 citation statements)

References 18 publications

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“…Most studies on GA application often consider the use of either RCGA or binary coding methods (Javanmard and Koraeizadeh 2016). In this study, RCGA is selected over binary coded GA, the reason for selecting RCGA is that it performed better than binary-coded GA.…”

Section: Meta-heuristicsmentioning

confidence: 99%

An enhanced reliability-oriented workforce planning model for process industry using combined fuzzy goal programming and differential evolution approach

2017

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This paper draws on the ''human reliability'' concept as a structure for gaining insight into the maintenance workforce assessment in a process industry. Human reliability hinges on developing the reliability of humans to a threshold that guides the maintenance workforce to execute accurate decisions within the limits of resources and time allocations. This concept offers a worthwhile point of deviation to encompass three elegant adjustments to literature model in terms of maintenance time, workforce performance and return-on-workforce investments. These fully explain the results of our influence. The presented structure breaks new grounds in maintenance workforce theory and practice from a number of perspectives. First, we have successfully implemented fuzzy goal programming (FGP) and differential evolution (DE) techniques for the solution of optimisation problem in maintenance of a process plant for the first time. The results obtained in this work showed better quality of solution from the DE algorithm compared with those of genetic algorithm and particle swarm optimisation algorithm, thus expressing superiority of the proposed procedure over them. Second, the analytical discourse, which was framed on stochastic theory, focusing on specific application to a process plant in Nigeria is a novelty. The work provides more insights into maintenance workforce planning during overhaul rework and overtime maintenance activities in manufacturing systems and demonstrated capacity in generating substantially helpful information for practice.

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Section: Meta-heuristicsmentioning

confidence: 99%

An enhanced reliability-oriented workforce planning model for process industry using combined fuzzy goal programming and differential evolution approach

2017

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“…There for, we are looking for the shortest distance traveled by maintenance team starting from the maintenance operation that passed by all assets before their RUL. GA are stochastic-based search techniques particularly suitable for solving complex optimization problems [29], GA are applied to maintenance optimization because of their robust search capabilities that resolve the computational complexity of large-size optimization problems [30]. In [31] GA are used, as an optimization tool to compare the cost of premature replacement with the cost of downtime if grounded for the sole purpose of replacement.…”

Section: A Problem Statementmentioning

confidence: 99%

Post-prognostics decision in cyber-physical systems

Meraghni

Terrissa

Ayad

et al. 2018

2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET)

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Prognostics and Health Management (PHM) offers several benefits for predictive maintenance. It predicts the future behavior of a system as well as its Remaining Useful Life (RUL). This RUL is used to planned the maintenance operation to avoid the failure, the stop time and optimize the cost of the maintenance and failure. However, with the development of the industry the assets are nowadays distributed this is why the PHM needs to be developed using the new IT. In our work we propose a PHM solution based on Cyber physical system where the physical side is connected to the analyze process of the PHM which are developed in the cloud to be shared and to benefit of the cloud characteristics

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“…Meta-heuristics are popular optimization algorithms, often used to solve large-scale complex optimization problems in various fields (Abtahi and Bijari 2016;Javanmard and Koraeizadeh 2016;Moradgholi et al 2016). In the research of Shao et al (2009), a genetic algorithmbased method was used for optimization of two functions: process planning and scheduling.…”

Section: Literature Reviewmentioning

confidence: 99%

An improved genetic algorithm for multidimensional optimization of precedence-constrained production planning and scheduling

2017

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Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial, NP-hard problem, for which no polynomial time algorithm is known to produce an optimal result on a random graph. In this paper, the further development of Genetic Algorithm (GA) for this integrated optimization is presented. Because of the dynamic nature of the problem, the size of its solution is variable. To deal with this variability and find an optimal solution to the problem, GA with new features in chromosome encoding, crossover, mutation, selection as well as algorithm structure is developed herein. With the proposed structure, the proposed GA is able to ''learn'' from its experience. Robustness of the proposed GA is demonstrated by a complex numerical example in which performance of the proposed GA is compared with those of three commercial optimization solvers.

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Optimizing the preventive maintenance scheduling by genetic algorithm based on cost and reliability in National Iranian Drilling Company

Cited by 22 publications

References 18 publications

An enhanced reliability-oriented workforce planning model for process industry using combined fuzzy goal programming and differential evolution approach

An enhanced reliability-oriented workforce planning model for process industry using combined fuzzy goal programming and differential evolution approach

Post-prognostics decision in cyber-physical systems

An improved genetic algorithm for multidimensional optimization of precedence-constrained production planning and scheduling

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