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All Journal Civil Engineering Dimension Dimensi Utama Teknik Sipil Jurnal Teknik Sipil
Reynaldo, Ambrosius Matthew Junius
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Civil Engineering, Building, Construction & Architecture

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Vehicle Routing Problem Optimization for Rebar Material Distribution using the Symbiotic Organisms Search Method Reynaldo, Ambrosius Matthew Junius; Husada, Willy; Wijaya, Ezra Kenzie; Vaphilio, Denish
Civil Engineering Dimension Vol. 27 No. 2 (2025): SEPTEMBER 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/ced.27.2.203-213

Abstract

The success rate of construction projects depends on subcontractors and material suppliers, especially in ensuring the material delivery to avoid delays and cost overruns. The Vehicle Routing Problem (VRP) addresses transportation management to minimize the distribution costs. This study presents a comparative analysis of three VRP scenarios: the existing case, the Capacitated Vehicle Routing Problem (CVRP), and the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). The Symbiotic Organisms Search (SOS) method is used to solve the VRP of a building materials supplier in Sulawesi, Indonesia in delivering rebar to 19 locations over 12 weeks while considering the vehicle capacity and the time window constraints. The results show that the SOS method effectively handles the rebar distribution problems with these constraints. The CVRP scenario achieves a total cost saving of Rp 23,263,278 (26.15%), while the CVRPTW scenario saves Rp 6,732,942 (7.57%).
Optimasi Penjadwalan Proyek Kantor PT.X dengan Konsep Finance-Based Scheduling dan Metode Metaheuristik Reynaldo, Ambrosius Matthew Junius; Prayogo, Doddy; Thendean, Richard Christian; Freando, Imannuel Michraga
Jurnal Teknik Sipil Vol 20 No 2 (2024): Jurnal Teknik Sipil
Publisher : Universitas Kristen Maranatha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jts.v20i2.8445

Abstract

Contractors need to pay attention to the financial condition of the company so that the availability of funds to carry out profitable construction operations can be fulfilled. Construction projects require substantial funds, so contractors often utilize bank loan systems. A finance-based scheduling concept is needed to combine scheduling and funding in construction projects. This research seeks to optimize finance-based scheduling with the goal of minimizing interest on contractor loans. The optimization method used is the metaheuristic algorithm Symbiotic Organisms Search (SOS), and the case study used is the construction project of PT.X office building in Surabaya with the aim of providing alternative scheduling scenarios to contractors. The optimization process involves shifting the start time of activities (shift value) to generate new schedules. The results of the optimization process in three payment scenarios were able to produce smaller interest burdens on contractor loans. Additionally, after the optimization process, the contractor's largest loan also decreased, meeting the credit limit set by the bank. The best alternative scenario choices are scenarios 1 and 3, which result in the highest decrease in loan interest burden and the largest decrease in loan amount.
PREDIKSI RISIKO KETERLAMBATAN PADA PROYEK BANGUNAN TINGGI DI SURABAYA MENGGUNAKAN BERBAGAI METODE MACHINE LEARNING Reynaldo, Ambrosius Matthew Junius; Suryawan, Nathanael Kenneth; Husada, Willy
Dimensi Utama Teknik Sipil Vol. 13 No. 1 (2026): April 2026
Publisher : Program Studi Magister Teknik Sipil - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/duts.13.1.95-114

Abstract

Industri konstruksi merupakan kontributor utama pertumbuhan ekonomi suatu negara yang memiliki karakteristik unik karena memiliki banyak variasi bergantung pada karakteristik setiap proyek. Tingginya kompleksitas proyek menyebabkan peningkatan risiko keterlambatan proyek. Penelitian ini melakukan prediksi risiko keterlambatan pada proyek bangunan tinggi di Surabaya dengan metode Artificial Neural Network (ANN), Support Vector Machine (SVM), dan Classification and Regression Tree (CART). Sebanyak 35 data proyek di Surabaya dihimpun melalui penyebaran kuesioner dengan 21 variabel input faktor risiko penyebab keterlambatan proyek dan 1 variabel output yaitu tingkat keterlambatan proyek. Pembuatan model prediksi dilakukan dengan transformasi data variabel input menjadi 3 model yaitu probability x impact, matriks risiko 5 zona, dan matriks risiko 3 zona. Proses evaluasi metode prediksi dilakukan dengan 7 parameter evaluasi. Hasil penelitian menunjukkan bahwa metode ANN dengan kombinasi model 2 memiliki kinerja terbaik dibandingkan dengan kedua metode prediksi lainnya yang menghasilkan tingkat akurasi 100% dalam melakukan klasifikasi tingkat keterlambatan proyek.
Co-Authors Freando, Imannuel Michraga Prayogo, Doddy Suryawan, Nathanael Kenneth Thendean, Richard Christian Vaphilio, Denish Wijaya, Ezra Kenzie Willy Husada