Condition-based Maintenance Policy Optimization Using Genetic Algorithms and Gaussian Markov Improvement Algorithm

Open Access
Hoffman, Michael L
Graduate Program:
Industrial Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
November 08, 2018
Committee Members:
  • Soundar Kumara, Thesis Advisor
  • Eunhye Song, Thesis Advisor
  • Michael Brundage, Committee Member
  • condition-based maintenance
  • maintenance optimization
  • genetic algorithm
  • gaussian markov improvement algorithm
Condition-based maintenance involves monitoring the degrading health of machines in a manufacturing system and scheduling maintenance to avoid costly unplanned failures. As compared with preventive maintenance, which maintains machines on a set schedule based on time or run time of a machine, condition-based maintenance attempts to minimize the number of times maintenance is performed on a machine while still attaining a prescribed level of availability. Condition-based methods save on maintenance costs and reduce unwanted downtime over its lifetime. Finding an analytically-optimal condition-based maintenance policy is difficult when the target system has non-uniform machines, stochastic maintenance time and capacity constraints on maintenance resources. In this work, we find an optimal condition-based maintenance policy for a serial manufacturing line using a genetic algorithm and the Gaussian Markov Improvement Algorithm, an optimization via simulation method for a stochastic problem with a discrete solution space. The effectiveness of these two algorithms will be compared. When a maintenance job (i.e., machine) is scheduled, it is placed in a queue that is serviced with either a first-in-first-out discipline or based on a priority. In the latter, we apply the concept of opportunistic window to identify a machine that has the largest potential to disrupt the production of the system and assign a high priority to the machine. A test case is presented to demonstrate this method and its improvement over traditional maintenance methods.