Resolving MCLP Using Walking Graph Network and Genetic Algorithm for the Proposal of Elder Facilities in Gwanak
The final submission for 2022-F CEE Urban Design Final Project
While Gwanak-gu has the fifth-highest percentage of elderly residents living alone in districts of Seoul, there are few elderly leisure facilities, therefore polarization is extreme. In this project, a walking graph network and genetic algorithm were used to offer new locations for elderly leisure facilities in Gwanak-gu. This study suggests a novel approach to calculate the score of the distributed locations using graph theories to solve MCLP problem.
This project used the data from map.seoul.go.kr
- Distribution of Elder Populations
- Facility Supply Map
- Facility Demand Map
This project used QGIS to obtain coordinates from the tif images and combined them together
Suggested Formula is below.
The objective function had to be created since there was no appropriate reference for the objective function that was to be used with the genetic algorithm. With a graph network, this formula was suitable as an objective function since it could perform complicated computations of the 4390X4390-sized matrix, result in quantitative values, and reflect the characteristics of the adjacent matrix. S is the importance score, L is the NX1 matrix of the location candidate obtained from the genetic algorithm's process, and D is the physical distance that cannot be represented on the node. These scores for each node are weighted to obtain the score of the location selection itself. One scalar-type score is produced on the graph by a matrix product.
The result is below.
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