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Optimization model for an airline crew rostering problem: Case of garuda Indonesia
Hadianti R.a, Novianingsih K.a,b, Uttunggadewa S.a, Sidarto K.A.a, Sumarti N.a, Soewono E.a
a Department of Mathematics, Institut Teknologi Bandung, Indonesia
b Department of Mathematics, Universitas Pendidikan Indonesia, 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]This paper discusses the cockpit crew rostering problem at Garuda Indonesia, taking into account a number of internal cockpit crew labor regulations. These internal labor regulations are in general more restrictive at Garuda Indonesia than at other airlines, so that modeling the cockpit crew rostering problem for Garuda Indonesia is challenging. We have derived mathematical expressions for the cockpit crew labor regulations and some technical matters. We model a non-linear integer programming for the rostering problem, using the average relative deviation of total flight time to the ideal flight time as the objective function. The optimization model have been tested for all classes of cockpit crews of Garuda Indonesia, using a simulated annealing method for solving the problem. We obtained satisfactory rosters for all crew members in a short amount of computing time. This shows that the optimization problem is well-defined. © 2013 Published by ITB Journal Publisher.[/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]Crew labor regulation,Crew pairing,Crew rostering,Crew scheduling,Optimization,Simulated annealing[/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/10.5614/j.math.fund.sci.2013.45.3.2[/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]