On the optimization of opportunistic maintenance activities
Licentiate thesis, 2010
Maintenance is a source of large costs; in the EU the maintenance costs amount
to between 4% and 8% of the total sales turnover. Opportunistic maintenance is an
attempt to lower the maintenance cost by considering the failure of one
component as an opportunity to replace yet non-failed components in order to
prevent future failures. At the time of failure of one component,
a decision is to be made on which additional components to
replace in order to minimize the expected maintenance cost over a planning
This thesis continues the work of Dickman et. al. (1991) and Andreasson (2004) on the opportunistic replacement problem. In Paper I, we show that
the problem with time-dependent costs is NP-hard and present a mixed integer
linear programming model for the problem. We apply the model to problems with
deterministic and stochastic component lives with data originating from the
aviation and wind power industry. The model is applied in a stochastic setting
by employing the expected values of component lives. In Paper II, a first step
towards a stochastic programming model that considers components with uncertain lives is
taken by extending the problem to allow non-identical lives for component individuals.
This problem is shown to be NP-hard even with time-independent costs. We present a
mixed integer linear programming model of the problem. The solution time of the model
is substantially reduced compared to the model presented in Andreasson (2004). In Paper III, we then study the opportunistic
replacement problem with uncertain component lives and present a two-stage
stochastic programming approach. We present a deterministic equivalent model and
develop a decomposition method.
Numerical studies on the same data as in Paper I
from the aviation and wind power industry show that the stochastic programming approach
produces maintenance decisions that are on average less costly than
decisions obtained from simple maintenance policies and the approach used in Paper I.
The decomposition method requires less CPU-time than solving
the deterministic equivalent on three out of four problems.
mixed integer linear programming