A Framework of Genetic Algorithm Improvement for Optimal Block Division in Lining Layout Planning

This study focuses on the Genetic Algorithm (GA) as solution strategy for block division in Lining Layout Planning (LLP). Block division is optimal when the combined shapes in an area promote no empty space. This problem requires huge number of possible solutions to be analyzed and it is considered as a set of space allocation problems. Classical Genetic Algorithm (CGA) with basic operators was applied to find optimal solution. Despite CGA is able to promote the optimal result however it has opportunity to improve time efficiency. Therefore, a framework of GA improvement (IGA) for block division was introduced by looking into the genes of chromosome for problem representation and prior to the processes of crossover and mutation. The IGA involves three strategies which are; (1) specific random value for chromosome representation, (2) deterministic crossover is to avoid from the same result of crossover process and (3) deterministic mutation is to protect overlapping shapes. This paper reported the theoretical analysis of possible improvements and then generates results from the various coordinates of areas to evaluate the performance of the CGA and IGA. The overall result presents that IGA promoted fewer number of repetitions than CGA and as a result IGA expedites processing time to obtain optimal result.