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

Abd al-Wahhab Koraeizadeh,Habibollah Javanmard

doi:10.1007/s40092-016-0155-9

Abd al-Wahhab Koraeizadeh, Habibollah Javanmard

Open Access

https://doi.org/10.1007/s40092-016-0155-9

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Abstract

The present research aims at predicting the required activities for preventive maintenance in terms of equipment optimal cost and reliability. The research sample includes all offshore drilling equipment of FATH 59 Derrick Site affiliated with National Iranian Drilling Company. Regarding the method, the research uses a field methodology and in terms of its objectives, it is classified as an applied research. Some of the data are extracted from the documents available in the equipment and maintenance department of FATH 59 Derrick site, and other needed data are resulted from experts’ estimates through genetic algorithm method. The research result is provided as the prediction of downtimes, costs, and reliability in a predetermined time interval. The findings of the method are applicable for all manufacturing and non-manufacturing equipment.

Highlights

As the trade-off between the preventive maintenance costs and corrective maintenance requires different methods, the present research focuses on a schedule that cuts the costs and keeps the reliability at an acceptable level; the optimization of the task is undertaken through genetic algorithm
A handful of researches have devoted to the preventive maintenance scheduling in the recent years, most of which are attempts for coordinating preventive maintenance scheduling with the production line (Moghaddam and Usher 2011; Fitouhi and Nourelfath 2012; Nourelfath and Chatelet 2012)
The main advantage of this method is revealed in providing updated schedule consistent with the system life cycle at a pre-determined time interval

Summary

Introduction

As the trade-off between the preventive maintenance costs and corrective maintenance requires different methods, the present research focuses on a schedule that cuts the costs and keeps the reliability at an acceptable level; the optimization of the task is undertaken through genetic algorithm.A handful of researches have devoted to the preventive maintenance scheduling in the recent years, most of which are attempts for coordinating preventive maintenance scheduling with the production line (Moghaddam and Usher 2011; Fitouhi and Nourelfath 2012; Nourelfath and Chatelet 2012). Most of the schedules were originally developed for power plants but shortly after that the optimized scheduling was employed for mechanical components (Tsai et al 2001) and for production lines (Sortrakul et al 2005). It has not been used in the drilling industry

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