Topic: Şebnem Manolya Demir Tez Sunumu
Time: Jul 7, 2022 05:00 PM Istanbul
https://zoom.us/j/6547746234?pwd=ZENZNWtCbUlQRjVMMVFneWtxZGlzZz09
Meeting ID: 654 774 6234
Passcode: 478379
Title: A New Selective Location Routing Problem: Educational Services for Refugees
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 been 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, first, we identify the availability and accessibility challenges posed by the country's existing plans of integrating refugees to the national education system. To reinforce schooling access for the refugee children in Turkey, then, 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 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 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 CCMCP-HC and Modular CMCP-HC improve schooling enrollment rates and capacity utilizations compared to status quo. Moreover, SLRP ad 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 possible and similar situations in the future.
