Exact and heuristic algorithms for post prognostic decision in a single multifunctional machine

Asma LADJ, Christophe Varnier, Fatima Benbouzid Si Tayeb, and Noureddine Zerhouni
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_17_015.pdf1.85 MBSeptember 16, 2017 - 12:48pm

Prognostic and Health Management (PHM) benefits are strongly tied to the decision-making that follows the assimilation and interpretation of prognostics information. Hence, we deal in this study with the post prognostic decision making in order to improve system safety and avoid downtime and inopportune maintenance spending. We investigate the problem of scheduling production jobs in a single multifunctional machine subjected to predictive maintenance based on PHM results. For this reason, we propose a new interpretation of PHM outputs to define the machine degradation corresponding to each job. We develop a Mixed Integer Linear Programming (MILP) model to find the best integrated scheduling that optimizes the total maintenance cost. Unfortunately, the MILP is not able to compute the optimal solution for large instances. Therefore, we design a Prognostic based Genetic Algorithm (Pro-GA). Computational results of different benchmarks setup show the efficiency and robustness of our scheme with an average deviation of about 0.2% over a newly proposed lower bound.

Publication Year: 
2017
Publication Volume: 
8
Publication Control Number: 
015
Page Count: 
15
Submission Keywords: 
Post-prognostics decision
degradation
predictive maintenance
Maintenance Scheduling
Genetic Algorithm optimization
Mixed Integer Linear Programming
Submission Topic Areas: 
Automated reconfiguration
Industrial applications
Submitted by: 
  
 
 
 

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