Optimization Of Jakarta Container Terminal Operational Services Performance Using Genetic Algorithm And Particle Swarm Optimization (GA-PSO)

Sumadilaga, Ibrahim Tirta (2025) Optimization Of Jakarta Container Terminal Operational Services Performance Using Genetic Algorithm And Particle Swarm Optimization (GA-PSO). Masters thesis, Institut Technology Sepuluh Nopember.

Text 6020201006-Master_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 April 2027. Download (9MB) | Request a copy

Abstract

Pertumbuhan ekonomi yang pesat mendorong peningkatan aktivitas di pelabuhan. Pelabuhan peti kemas, sebagai salah satu faktor kunci dalam penilaian kinerja pelabuhan, memerlukan pengelolaan yang efisien. Penelitian ini bertujuan mengoptimalkan penggunaan alat bongkar muat seperti quay crane, headtruck, dan rubber tyred gantry di Terminal Petikemas Jakarta. Dengan menggunakan metode Genetic Algorithm (GA) dan Particle Swarm Optimization (PSO), penelitian ini berupaya mencari solusi optimal dan perencanaan untuk kedepan sehingga dapat memaksimalkan produktivitas dan meminimalkan penggunaan alat. Hasil dari penelitian ini, data aktual menunjukkan hasil yang baik ET:BT sebesar 87,49% dan BOR sebesar 77,92%, Sedangkan ET:BT menggunakan GA lebih optimal (94,27%) dibandingkan PSO (59,45%), GA lebih efisien dalam penggunaan peralatan dan produktivitas alat yang lebih tinggi secara keseluruhan, sementara PSO menawarkan potensi waktu berthing time yang lebih singkat dan nilai Fx yang lebih baik dalam beberapa skenario, meskipun dengan kebutuhan peralatan yang lebih banyak.
===============================================================================================================================
The continuous growth of the economy contributes to increased port activity. Container terminals, which play a crucial role in evaluating port performance, require efficient management. This research aims to optimize the use of handling equipment such as quay cranes, headtrucks, and rubber tyred gantry at Jakarta Container Terminal. By employing Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods, this research aims to identify optimal solutions for maximizing productivity and minimizing equipment requirements. Results from this research indicate that the actual data yields favorable results with ET:BT of 87.49% and BOR of 77.92%. However, using GA, the ET:BT becomes more optimal (94.27%) compared to PSO (59.45%). GA proves more efficient in overall equipment utilization and productivity, while PSO offers the potential for shorter berthing times and better Fx values in certain scenarios, albeit with a higher equipment requirement.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Optimasi, Genetic Algorithm, Particle Swarm Optimization, Optimization
Subjects:	T Technology > TS Manufactures > TS155 Production control. Production planning. Production management
Divisions:	Faculty of Marine Technology (MARTECH) > Ocean Engineering > 38101-(S2) Master Thesis
Depositing User:	Ibrahim Tirta S
Date Deposited:	01 Feb 2025 08:36
Last Modified:	01 Feb 2025 08:36
URI:	http://repository.its.ac.id/id/eprint/117376

Actions (login required)

View Item