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Inland container depots effect for import container terminal performance at Koja container terminal, Jakarta based on optimization-simulation model
Rusgiyarto F.a, Sjafruddin A.a, Frazila R.B.a, Suprayogia, Burhani J.T.a
a Civil Engineering Post-Graduate Department, Civil Engineering and Environment Faculty, Institut Teknologi Bandung, 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]© 2018 Author(s).Increasing the container volume and the lack of land acquisition for terminal development is a problem encountered by container terminal operators in Indonesia in the last years. To maintain the high demand on terminal service, the operator uses Inland Container Depots (ICD) as one of the fastest and cheapest solutions with some limitations compared to port expansion. Capacity and location are issues that should be addressed when ICD is used as one of the capacity-solving problems. ICD operation will increase the ability of the terminal services, yet the connecting roads that are also used by non-container traffic will affect the transfer operation of containers from the terminal to ICD. The container movement from the terminal to ICD is determined by the value of Yard Occupancy Ratio (YOR). The method that integrates the decision of transferring containers from the terminal and selecting ICD locations is required to cope with this issue. Differences in cycle time equilibrium of terminal operations and connecting roads complicate the construction of analytical equations. Discrete Event Simulation is used to accommodate the difference of operating time cycle, a stochastic condition of arrival, service time, an integration of terminal operation process and ICD location selection. The case study of Koja Container Terminal was used to perform experimental test of the proposed method. The simulation model was constructed for one month of simulation. Level of demand and ICD capacity were factors to be tested in the model to calculate the effects of ICDs operation on the container terminal. Optimum Terminal – ICD configuration was chosen by maximizing the container cost difference of without and with ICDs. Total container cost consists of the container handling cost (discharge, transfer, lift on, lift off, custom check and storage costs) and the container time cost (waiting time and potentially demand unserved). Since the addition amount of ICDs will increase the handling cost and decrease the time cost, so, there are appropriate ICDs numbers for a certain level of demand. Experimental result of Koja container terminal showed that the optimum amount of ICDs will decrease the total container cost charged to the users for the demand level above the terminal capacity. Terminal throughput will increase but the dwell time will not reduce significantly. The optimum configuration for the condition is Terminal – ICD (Yos Sudarso) and ICD (LLRE Martadinata). Optimization based on Discrete Event Simulation can be used to obtain the optimum configuration of ICD terminal system at certain demand level and to measure the ICD effect on terminal performance, ICD and Connecting Road. Operation of the ICD will decrease the container cost but will not provide significant dwell time for terminal improvements.[/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][/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][/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]The authors would like to acknowledge the LPPM ITB for the P3MI Research Grant and Koja Container Terminal for the data support of the paper.[/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/10.1063/1.5042894[/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]