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Maintenance parameters based production policies optimization

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Purpose ‐ Planning of manufacturing and maintenance activities together, creating a balance between maintenance and production parameters and developments on maintenance will prevent technical and economic losses and increase production efficiency. Optimizing production and maintenance scheduling enable us to see how maintenance parameters (ß,??,?t p ,?t r ,?a [o]) will affect production performance, completion time (Ec.) and maximum machine availability, and shows which maintenance parameters minimum completion time (Ecmin) will be provided. Difference between Ecmin and maximum completion time (Ecmak) effect to the production costs will be calculated. The purpose of this paper is to show how a genetic algorithm (GA) procedure is successfully applied to the integrated optimization model to determine optimum production policies based maintenance parameters. Design/methodology/approach ‐ GA is used for optimization and a computer program is prepared to make optimization for integrated preventive maintenance and production planning (IPMPP). Using the program, experimental studies are carried out with different number of jobs be done, to optimize production policy taking maintenance parameters into account. Findings ‐ Numerous experiments have been conducted with developed GA computer program and see maintenance parameters (ß,??,?t p ,?t r ,?a [o]) effect to the production performance, Ec and maximum machine availability and at which maintenance parameters Ecmin will be provided, and also operating cost saving and maintenance parameters how affect Ec subjects are examined. Due to optimal preventive maintenance (PM) and production sequence arrangement and application of PM provided by GA, Ecmin is greatly decreased. Originality/value ‐ In this paper, GA procedure is successfully applied to the integrated optimization model to determine optimum production policies based maintenance parameters.
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Keywords: Genetic algorithms; Maintenance; Operations and production management; Optimization; Production and maintenance planning

Document Type: Research Article

Publication date: 2012-08-10

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