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the number of flights increases, the risk decreases but
the travel time increases.
In the GA solutions with low risk, each flight first
performs deliveries, and then it starts with pickups af-
ter finishing all the deliveries. All installations are
visited twice, expect those at which the helicopters
perform their last delivery and their first pickup.
In the GA solutions with low travel time, Hamil-
tonian routes are often identified. The installations in
Hamiltonian routes are visited only once to simulta-
neously perform pickup and delivery.
5 CONCLUSIONS
We have addressed a helicopter routing problem aris-
ing in the transportation of offshore employees as
a multi-objective problem, in which the risk, the
cost and the number of flights objectives are consid-
ered. A genetic algorithm is applied to the problem
and it is evaluated by comparing with ε-constraint
approach with a single-objective tabu search meta-
heuristic. Preliminary case study shows that the ge-
netic algorithm can generate high quality solution, in
terms of the spread and convergence of solutions.
ACKNOWLEDGEMENTS
Thanks are due to the referees for their valuable com-
ments.
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