Model Stokastik Transportasi Laut Komoditas Fatty Acid Methyl Ester (FAME) Untuk Mendukung Kebijakan Biodiesel B100

Oktaviana, Rizka (2021) Model Stokastik Transportasi Laut Komoditas Fatty Acid Methyl Ester (FAME) Untuk Mendukung Kebijakan Biodiesel B100. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

[img] Text
04411740000030-Undergraduate_Theses.pdf - Accepted Version
Restricted to Repository staff only until 1 October 2023.

Download (3MB) | Request a copy

Abstract

Saat ini kebijakan mandatori BBN mencapai B30 dan akan berlanjut pada B50 hingga biodiesel 100% (B100) pada tahun 2025 -2030 sebagai bahan bakar substitusi solar yang akan diterapkan di berbagai sektor. Dalam studi penelitian ini dilakukan perencanaan proses distribusi meliputi rute distribusi, pemilihan moda kapal, dan penentuan jumlah kebutuhan kapal sehingga dapat dihasilkan perencanaan yang optimum. Namun dalam memproyeksikan perencanaan transportasi laut di masa yang akan datang, terdapat variabel randomness yang mempengaruhi proses pengambilan keputusan sehingga hanya dapat dilakukan dengan pendekatan rumusan model stokastik. Berdasarkan permasalahan tersebut, pada penelitian ini dilakukan analisis replikasi proyeksi permintaan menggunakan metode simulasi monte carlo. Selanjutnya dilakukan penenetuan rute terdekat menggunakan metode travelling salesman problem. Kemudian pemilihan kapal optimum yang dapat melayani menggunakan metode optimasi bin packing. Dari hasil replikasi, didapatkan informasi variabel yang mempengaruhi stokastik dalam perencanaan transportasi laut meliputi permintaan, payload kapal, dan ukuran utama kapal yang dirumuskan dengan metode Euler. Hasil yang diperoleh dalam penelitian ini yaitu untuk variabel random pada permintaan yang dirumuskan dengan dX15 = 241.425(X14)dt + 37.655(X14)dWt (batas alpha 0,479 dan beta 0,678) yang mengakibatkan stokastik dalam transportasi laut sehingga dapat membantu dalam pendekatan perencanaan transportasi laut. ===================================================================================================== Currently, the mandatory biofuel policy reaches B30 and will continue at B50 to 100% biodiesel (B100) in 2025-2030 as a substitute for diesel fuel which will be applied in various sectors. In this research study, the distribution process planning includes distribution routes, ship mode selection, and determination of the number of ship needs so that optimum planning can be produced. However, in projecting marine transportation planning in the future, there is a randomness variable that affects the decision-making process so that it can only be done with a stochastic model formulation approach. Based on these problems, in this study, a replication analysis of demand projections was carried out using the Monte Carlo simulation method. Next, the closest route is determined using the traveling salesman problem method. Then the selection of the optimum ship that can serve using the bin packing optimization method. From the results of replication, information on variables that affect stochastic in marine transportation planning includes demand, ship payload, and the main size of the ship formulated by the Euler method. The results obtained in this study are for random variables on demand which are formulated with dX15 = 241.425(X14)dt + 37.655(X14)dWt (limit alpha 0.479 and beta 0.678) which result in stochasticity in sea transportation so that it can assist in marine transportation planning approaches.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: FAME, Model Stokastik, Transportasi Laut, Variabel Random, Stochastic Model, Sea Transportation, Random Variable
Subjects: Q Science > QA Mathematics > QA274.2 Stochastic analysis
Q Science > QA Mathematics > QA402.6 Transportation problems (Programming)
V Naval Science > V Naval Science (General)
Divisions: Faculty of Marine Technology (MARTECH) > Marine Transportation Engineering > 21207-(S1) Undergraduate Thesis
Depositing User: Rizka Oktaviana
Date Deposited: 26 Aug 2021 12:25
Last Modified: 26 Aug 2021 12:25
URI: https://repository.its.ac.id/id/eprint/89744

Actions (login required)

View Item View Item