locomotives is totally decreased by 35% and it has a
significant impact on reducing costs as well.
Moreover, the detailed schedule of each locomotive
including the departure time, trains in its
commitments, planned routes, waiting times and etc
is corresponding to the routes found by the proposed
VRPTW and they are identified for this route.
6 CONCLUSIONS
In this paper, a new multi-objective dynamic vehicle
routing and scheduling problem has been presented
and solved. To solve this multi-objective model, an
evolutionary algorithm has been and its performance
has been analyzed on various test problems. The
results show the efficiency and effectively of
proposed method. Finally, the real case study has
been considered by the proposed model as well and
it has been analyzed.
ACKNOWLEDGEMENTS
The authors would like to thank MAPNA Group for
its supports and financing this paper.
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