Proposing a More Efficient Maintenance Scheduling for an Overhaul Maintenance Project in Engineering Asset Management

In maintenance activity, scheduling planning is an important step to improve the performance of maintenance department. In some cases, the data to support the process to develop the maintenance schedule is unavailable. In this research, it is argued that Genetic Algorithm (GA) is an appropriate tool...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2020-01, Vol.722 (1), p.12071
Main Authors: Cahyo, W N, Hasibuan, F W, Hendradewa, A P
Format: Article
Language:English
Subjects:
Asset management
Decision making
Fitness
Genetic algorithms
Maintenance management
Manpower
Parameters
Programming languages
Project management
Schedules
Scheduling
Sugarcane
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Summary:In maintenance activity, scheduling planning is an important step to improve the performance of maintenance department. In some cases, the data to support the process to develop the maintenance schedule is unavailable. In this research, it is argued that Genetic Algorithm (GA) is an appropriate tool to support the decision maker in order to develop the schedule. The data required in this research is obtained from the maintenance data in the sugar milling company in Yogyakarta. The company run the production of sugar and spirits/alcohol. The Production process (milling seasin) is usually performed within the harvesting months of the raw material: the sugar cane. It is approximately in the month of February to August. On the other hand, the overhaul maintenance process is performed after the milling season. The overhaul maintenance planning is created by the manager of the maintenance department, and usually is done using the manager qualitative approach without any sciencetific approach. To get a more efficient schedule of maintenance, several parameters are set in this research. The parameters are then applied as the paramenters in GA. Those are: The number of manpower required and the duration of operation each activity. To run the GA model, Python programming is used. Before start the GA process, the parent chromosom and its fitness are determined. The date of maintenance activity start per each activity is set as the parent chromosome and the fitness is determined using a formula. The process of finding the better maintenance schedule is then performed based on the procedures in GA. It is indicated from the result that the proposed schedule generated in GA required less number of manpower.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/722/1/012071