| dc.description.abstract | With the development of global integration, global trade makes it urgent for every country
to develop port and terminal infrastructure and logistics services. In ports and terminals,
transporting vehicles like yard cranes and yard trucks contribute greatly to smooth flow of
the goods and overall operation. This thesis has taken this problem into consideration and
come up with an optimized scheduling solution to minimize average container processing
time by applying meta-heuristic called Genetics Algorithms. A simulation model is then
developed in ARENA to test whether the suggested solution is applicable in stochastic
environments and make a comparison with several basic rules like FIFO, Smallest Distance,
Random. The results show that Genetics Algorithm performs very well in scheduling and
optimization but does not bring the same effect when comparing with other method in an
environment full of randomness. The problem of data integration between Programming
tools like Python and ARENA remains for future investigation | en_US |
