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Optimum scheduling system for container truck based on genetic algorithm on spin(Simulator Pelabuhan Indonesia)
Supeno H.a, Rusmin P.H.a, Hindersah H.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2015 IEEE.The logistic process of Container is very crucial to the port, but often problem occur due to lack of truck scheduling so that adds to the turn around time. Response to this problem, this thesis will try to generate scheduling trucks by genetic algorithm to minimizes the traveling distance when operating in unloading and loading cargo so can reduce the turn around time. This optimization will embedded to SPIN (Simulator Pelabuhan Indonesia) so user can see the result with 3d visualization. Because the genetic algorithm used in a simulator, the computing time becomes an important issue. Therefore, genetic algorithms built using several techniques, such as permutation with two part chromosome, greedy crossover, tournament selection, and simple swap mutation The test results of this study prove that by using the techniques described above, the time and distance it takes to complete the process of unloading is a decreased and computation time program is relatively small. From the results of experiments conducted it was concluded that genetic algorithms can reduce unloading time that there is a way to assign the truck to the next job that has the shortest distance. With shorter unloading time, the direct impact on the costs to be incurred by mooring the vessel so that the port becomes more underserved customers. Implementation of scheduling optimization truck simulator port has exciting potential to be developed, among others: 1. Usually the number of trucks that are assumed to be static parameters while in reality the number of trucks is dynamic because the truck can be increased or decreased (due to strike for example). Similarly, the condition of the equipment, eg the crane damaged, roads were jammed, and so on. Through this simulator can be adaptive and visualized. 2. With the successful implementation of the GA, it proves that the simulator can be used as a sort of template SPIN test optimization strategies. So that future SPIN can be implemented by various strategies. 3. With the ability to accelerate time, manipulate the situation, and add and reduce the object. SPIN can be used as forecasting tool and decision support systems for operators and management of container terminals.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Container terminal,Forecasting tools,port,Scheduling optimization,Scheduling systems,Simulation,Tournament selection,Two-part chromosomes[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]container,Crane,genetic algorithm,optimization,port,scheduling,Simulation,terminal,truck[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/IDM.2015.7516352[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]