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Research on integrated scheduling of equipment predictive maintenance and production decision based on physical modeling approach
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China Institute of FTZ Supply Chain, Shanghai MaritimeUniversity, China
School of Logistics Engineering, Shanghai Maritime University, China
Submission date: 2023-09-07
Final revision date: 2023-11-02
Acceptance date: 2023-11-17
Online publication date: 2023-11-19
Publication date: 2023-11-19
Corresponding author
Lei Yang   

School of Logistics Engineering, Shanghai Maritime University, Shanghai Maritime University, 1550 Ganghai Avenue,, 201306, Shanghai, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2024;26(1):175409
  • Consider the impact of equipment degradation maintenance on production decisions.
  • Physical modeling is used to analyze the equipment with imperfect fault data.
  • An improved genetic algorithm based on hormone regulation was used to solve the problem.
  • Integrating maintenance and production into a real production environment.
Equipment performance deteriorates continuously during the production process, which makes it difficult to achieve the expected effect of production decisions made in advance. Predictive maintenance and production decisions integrated scheduling aim to rationalise maintenance activities. It has been extensively researched. However, past studies have assumed that faults obey a specific probability distribution based on historical data. It is difficult to analyse equipment that is brand new into service or has poor historical failure data. Thus, in this paper, we construct a twin model of a device based on a physical modelling approach and tune it to ensure high fidelity of the model. Degradation curves were created based on equipment characteristics and developed maintenance activities.Develop an integrated scheduling model for predictive maintenance and production decisions with the goal of minimising maximum processing time. An improved genetic algorithm is used to solve the problem optimally. Finally, apply a practical scenario to verify the effectiveness of the proposed method.
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