TY  - JOUR
AB  - Describes the development of two genetic algorithm (GA) programs
for cost optimization of opportunity‐based maintenance policies. The
combinatorial optimization problem is formulated and it is shown that
genetic algorithms are particularly suited to this type of problem. The
theoretical basis and operations of a standard genetic algorithm (SGA)
are presented with an iterative procedure necessary for implementation
of the SGA to least‐cost part replacement. However, an SGA used in an
iterative manner may limit the global search capability of the
evolutionary computing technique and may lead to suboptimal solutions.
To avoid this problem, an improved GA which considers more than two
groups simultaneously is devised. This model is based on the permutation
representation and genetic sequencing operators originally developed for
the travelling salesman problem. The same example used with the SGA
confirmed that the improved GA can bring additional savings.
VL  - 1
IS  - 2
SN  - 1355-2511
DO  - 10.1108/13552519510089574
UR  - https://doi.org/10.1108/13552519510089574
AU  - Savic Dragan A.
AU  - Walters Godfrey A.
AU  - Knezevic Jezdimir
PY  - 1995
Y1  - 1995/01/01
TI  - Optimal opportunistic maintenance policy using genetic algorithms,
1: formulation
T2  - Journal of Quality in Maintenance Engineering
PB  - MCB UP Ltd
SP  - 34
EP  - 49
Y2  - 2024/04/18
ER  -