Management Science and Information Technology

Management Science and Information Technology

Scheduled Power Outage Constraint in Integrating Predictive Maintenance and Production Optimization in a Manufacturing System

Document Type : Research Paper

Authors
Hamid Mohammadi 1
Hossein Karimi 2
Rashed Sahraeian 1
1 Department of Industrial Engineering, Shahed University, Tehran, Iran
2 Department of Industrial Engineering, University of Bojnord, Bojnord, Iran
10.22034/ISS.2026.11282.1083
Abstract
Objective: The imbalance between energy supply and demand, particularly electricity, is a major challenge facing a large majority of industries in developing countries. Hence, proposing solutions to mitigate its adverse effects on production is critical. In this study, a data-driven predictive maintenance (PdM) system was developed to minimize production loss in a real multi-machine packaging system under scheduled power outage conditions. Unlike previous studies that relied on popular datasets, we used event-based historical data from machine failures.
Methods: Four ML models were constructed for failure prediction. Based on the results, a mixed-integer linear programming (MILP) model was proposed to determine optimal production and maintenance strategies. The framework was tested using real-world data collected over one year.
Results: The Deep Neural Network (DNN) outperformed the rest, achieving an accuracy of 99.6%, with a precision of 98.15%, recall of 92.98%, and F1-score of 95.5%. The proposed maintenance scenarios reduced total downtime by 5.3% and production loss by 5.0% in the 12-hour maintenance window, while the 24-hour maintenance window achieved reductions of 30.4% and 28.7%, respectively, compared with the baseline scenario.
Conclusion: This paper provides practical technical insights for maintenance professionals and recommendations for decision makers, especially in situations where nationwide production constraints may arise beyond organizational control.
Keywords
predictive maintenance
machine learning
failure prediction
deep neural networks
optimization
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Management Science and Information Technology
Volume 3, Issue 1
Winter 2026
Pages 30-48