dc.contributor.author | NISA, Nurmala Agita | |
dc.date.accessioned | 2024-07-15T01:26:33Z | |
dc.date.available | 2024-07-15T01:26:33Z | |
dc.date.issued | 2023-07-27 | |
dc.identifier.nim | 191810101020 | en_US |
dc.identifier.uri | https://repository.unej.ac.id/xmlui/handle/123456789/122180 | |
dc.description.abstract | Planning project activities is an important issue that needs to be considered. This is because planning serves as the foundation for completing a project in an optimal timeframe. The timing of construction project implementation must be designed optimally to avoid delays or excesses, as well as other impacts such as significant cost increases. Project scheduling is a part of the planning process that includes information about the planned schedule and project progress in terms of resource performance, including costs, workers, equipment, materials, and project duration. The scheduling model used in this research is the Precedence Diagram Method (PDM). PDM is a network diagram that falls under the Activity on Node (AON) group, which identifies critical paths. Critical paths are the paths of activities that must not be delayed. When it comes to accelerating the project duration, these critical paths are the ones that will be expedited. PDM calculations can be solved using the Genetic Algorithm approach. Genetic Algorithms are stochastic search algorithms based on natural selection and genetic mechanisms. Genetic Algorithms begin with an initial random set of solutions called a population, which represents the constraints of an optimization problem. This study used 10 iterations, where the population from iteration 1 is used as the population for iteration 2, and so on. To obtain the final chromosome, the same calculations as in iteration 1 are performed. Once the chromosomes for the entire population size are obtained, the chromosome with the minimum acceleration cost is selected. In the 10th iteration, the best chromosome with a fitness value of 0.2650 was found. This chromosome contains the genes 1 1 4 4 1 1, which means the optimal duration acceleration with the least cost increase is achieved by extending the working hours of Task A by 1 hour, Task B by 1 hour, Task C by 4 hours, Task D by 4 hours, Task E by 1 hour, and Task F by 1 hour. | en_US |
dc.language.iso | other | en_US |
dc.publisher | Fakultas Matematika dan Ilmu Pengetahuan Alam | en_US |
dc.subject | Precedence Diagram Method | en_US |
dc.subject | Biaya | en_US |
dc.subject | Waktu | en_US |
dc.subject | Algoritma Genetika | en_US |
dc.title | Penerapan Precedence Diagram Method (Pdm) Untuk Optimasi Penjadwalan dan Biaya Proyek dengan Pendekatan Algoritma Genetika | en_US |
dc.type | Other | en_US |
dc.identifier.prodi | Matematika | en_US |
dc.identifier.pembimbing1 | Dr. Agustina Pradjaningsih, S.Si., M.Si. | en_US |
dc.identifier.pembimbing2 | Abduh Riski, S.Si., M.Si. | en_US |
dc.identifier.validator | validasi_repo_iswahyudi_Mei_2024 | en_US |
dc.identifier.finalization | 0a67b73d_2024_07_tanggal 10 | en_US |