Document Type : Article
Authors
1
Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Malek Ashtar University of Technology, Isfahan, Iran
2
M.Sc. degree, Department of Industrial Engineering, Faculty of Engineering, Malek Ashtar University of Technology, Isfahan, Iran
10.24200/j65.2025.66986.2438
Abstract
Nowadays, due to globalization and intense competition, manufacturers face numerous challenges in maintaining their competitive advantages. They strive not only to reduce costs but also to increase system profitability. In production systems, due to increasing wear over time and machine aging (such as corrosion, deterioration, and breakage), many components experience failures. Therefore, the simultaneous optimization of maintenance and production planning has always been a subject of interest for many researchers. The production process involves various factors, including production and demand rates, shortage cost rates, and the planned time horizon, which significantly impact maintenance decision-making and vice versa. Moreover, the multi-component and complex nature of production systems cannot be overlooked. Thus, in this research, a simultaneous optimization model for maintenance and production planning of multi-component systems has been developed, considering safety stock and shortages, under a condition-based maintenance policy. The system produces a single type of product to meet constant demand within a finite time horizon. After each production cycle, inspections are conducted to determine the condition of components, and maintenance is performed if necessary. Since component degradation does not lead to immediate system stoppage but negatively affects performance, a criterion called structural importance has been utilized for component prioritization. Additionally, this study assumes that both preventive and corrective maintenance are carried out completely, and condition-based maintenance activities are based on preventive reliability and structural dependency of components. Inventory shortages are allowed and are considered as backorders. The objective of the model is to minimize the average cost rate, including: setup costs inspection costs, preventive and corrective maintenance costs, inventory holding costs, safety stock holding costs, replenishment costs and shortage costs. The model determines the optimal values of decision variables, including optimal production quantity, shortage amount, and preventive maintenance threshold. Finally, the proposed stochastic mathematical model is solved using a simulation-based optimization approach with Monte Carlo simulation.
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