A new selective location routing problem: educational services for refugees
Author(s)
Advisor
Yetiş, BaharDate
2022-07Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
217
views
views
17
downloads
downloads
Abstract
Syrian War has forced 5.5 million Syrians to seek for asylum. Turkey hosts 3.7
million Syrian refugees, 47% of whom are children. Even though the schooling
rate of Syrian refugee children has steadily increased, currently, there are
still more than 400 thousand children distanced from education. Turkey’s initial
plans were not accounting for a refugee crisis going on for a decade. In this
study, we first identify the availability and accessibility challenges posed by the
country’s existing plans of integrating refugees to the national education system.
Then, to reinforce schooling access for the refugee children in Turkey, we develop
a planning strategy that is aligned with the local regulations. To improve
school enrollment rates among Syrian refugee children without burdening the
existing infrastructure of the host country, we formulate Capacitated Maximal
Covering Problem with Heterogenity Constraints (CMCP-HC) and two extensions:
Cooperative CMCP-HC (CCMCP-HC) to improve the current schooling
access in Turkey and Modular CCMCP-HC to provide a guide for early planning
in the case of a future crisis. As lack of school accessibility has been identified
as one of the significant challenges hampering the school attendance rates, we
incorporate routing decisions. To ease children’s transportation to schools, we
propose a new Selective Location Routing Problem (SLRP) that corresponds to
a novel formulation, where the location decisions impact the selective nature of
the routing problem. For cases with further scarcity of the resources, we introduce
Attendance-based SLRP (A-SLRP) and represent children’s attendance
behaviors as a gradual decay function of distance. For the solution of these two
complex problems, we offer a 2-Stage Solution Approach that yields optimal solutions
for A-SLRP. Results of our computational analysis with the real-life data of the most densely refugee populated Turkish province illustrate that CCMCPHC
and Modular CMCP-HC improve schooling enrollment rates and capacity
utilizations compared to status quo. Moreover, SLRP and A-SLRP enable approximately
twice as many children’s continuation to education, compared to the
benchmarking formulation. Overall, this study analyzes Turkey’s experience and
lessons learned over a decade to provide a road-map based on operations research
methodologies, for potential similar situations in the future.
Keywords
RefugeesAccess to education
Maximal covering
Selective routing
Location routing
Humanitarian logistics