Genetic Algorithm-based Optimal Investment Scheduling for Public Rental Housing Projects in South Korea

Abstract

Declining birthrate is a serious problem that threatens the sustainability of Korean society. The main cause of this phenomenon is high living cost where housing cost accounts for the majority in household expenditure. South Korea has a very low supply rate in public rental housing when compared to other OECD countries. Because young people cannot afford to buy or lease a house for their new houses, some of them postpone or even give up marriage. As a countermeasure, Gyeonggi Province (surrounding area of Seoul) recently announced the supplying plan of 10,000 public rental houses by 2020. We expect this measure to alleviate the low birthrate problem and increase the demographic sustainability of the province. This study optimizes multi-annual investment scheduling for rental housing projects using genetic algorithm while satisfying the constraints such as budget, human resources, regional balance, etc. Through the optimal investment scheduling, we hope that public corporation will supply public rental houses more efficiently and more sustainably for the community.

Application of Computational Intelligence Techniques to an Environmental Flow Formula

“…While the goal of this paper to minimize any error between experimental data and corresponding hydraulic formula is accomplished by utilizing computational intelligence techniques, we have to consider more roughness-related data for implementing more practical and realistic formula in the future. Also, various soft computing techniques such as neural network [22], meta-heuristics [23], and fuzzy theory [24] will be utilized for this endeavor.…”

Optimal Project Planning for Public Rental Housing in South Korea

“…The Genetic algorithm was first introduced by John Holland in the 1960s [21]. GA is a technique for moving from one population of 'chromosomes' to a new population using selection operators such as crossover, mutation, and inversion.…”

“…Both studies used both the genetic algorithm and the branch and bound method to obtain combinatorial optimization solutions in real estate cases. Park et al [21] also proposed an optimization model for another type of real estate problem involving investment scheduling for public rental housing projects. However, their initial study has the limitation of not considering real-world cases.…”

Development of a Military Uniform Size System Using Hybrid Support Vector Clustering with a Genetic Algorithm

“…Ben-Hur et al [26] did not locate the optimal parameter. There are three related papers: Cho and Seo [33], Park et al [34], and Geem and Kim [35] that are worth mentioning. In this manuscript we apply a genetic algorithm (GA) to search for the optimal parameter such that we can reach the best effect for non-audit training.…”