Development of a simulation-based optimization approach to integrate condition-based maintenance, production control and control chart design in deteriorating production processes

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

2 Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan,Sanandaj, Iran

3 Industrial Engineering, Kermanshah University of Technology, Kermanshah, Iran

4 Department of mathematic, Faculty of Science, Mazandaran University of Science and Technology, Behshahr, Iran

10.22070/jqepo.2024.19061.1278

Abstract

A statistical model is proposed to schedule maintenance actions, control the inventory, and design a proper control chart in unreliable manufacturing systems. Process deterioration can lead to quality degradation and affect maintenance scheduling and production control. Integrated control of three aspects can enhance the productivity of production processes. A deteriorating manufacturing system is considered that has two states of in-control and out-of-control. Quality control of the products and process monitoring are implemented by employing a control chart. Maintenance actions are scheduled based on the machine’s condition. The duration of maintenance is considered a continuous random variable that follows a general distribution. The purpose is to schedule the maintenance tasks and control the safety stock with respect to the data collected from the control chart related to the machine condition to minimize the total cost per time unit. A Genetic algorithm (GA) is used as a solution method. Then, to reduce the solution time of the proposed GA, a Monte Carlo simulation method is presented. These two methods are combinted, and as a result a simulation-optimization technique is proposed. The performance of the model is validated by a simulation-based optimization technique. Finally, sensitivity analysis of the model is performed.

Keywords

References

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Journal of Quality Engineering and Production Optimization
Volume 8, Issue 2
December 2023
Pages 85-118

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