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Optimization of school network using location-allocation analysis: Case study: Bandung, Indonesia
Alifi M.R.a, Hayati H.a, Supangkat S.H.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, 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]© 2017 IEEE.The high school transportation costs urge the government of Bandung City to issue the policy of school attendance zone or rayonization. This policy requires a school network planning to minimize the school transportation costs. This paper proposes a school network planning to support the needs of this problem. This paper also considers the use of school participation and capacity estimation in the case of public schools as well as models capacity based recommendation based on the regulations of Indonesia Ministry of Education and Culture about minimum service standards of basic education. P-median problem is used as a model in the school network proposed. This study consists of three main phases, (1) Evaluation of existing condition, (2) Model of school allocation planning, and (3) Implementation of solutions. The allocation of school network for junior high schools in Bandung will be the case study in this paper. Bandung City has 30 districts, 151 sub-districts, 54 junior high schools, and 80,816 population of junior high school age level. Implementation of this study uses the analysis of location-allocation which is provided by network analyst extension in ArcGIS. The network proposed by this paper lowers 26.55% of impedance compared to the previous network. This paper also gives recommendations about reductions of school capacity in 11 districts, additions of school capacity in 2 districts, and constructions of new schools in 13 districts.[/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]Basic educations,Capacity estimation,Junior high schools,Location allocation,Ministry of Education,P-median problems,Service standards,Transportation cost[/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]GIS,network analysis,p-median problem[/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/TENCONSpring.2017.8070099[/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]