Optimization of RoRo (Roll On-Roll Off) Ship Berthing Scheduling Using the Berth Allocation Problem (BAP) Model at Parit Rempak Port, Karimun Regency

Authors

  • Nisha Desfi Arianti Universitas Karimun, Kabupaten Karimun, Kepulauan Riau, Indonesia
  • Mey Krisselni Sitompul Universitas Karimun, Kabupaten Karimun, Kepulauan Riau, Indonesia
  • Wulan Syahfina Universitas Karimun, Kabupaten Karimun, Kepulauan Riau, Indonesia
  • Selvika Putri Rahmania Universitas Karimun, Kabupaten Karimun, Kepulauan Riau, Indonesia
  • Ryryn Pransiska Universitas Karimun, Kabupaten Karimun, Kepulauan Riau, Indonesia
  • Leonardo Zerrand Universitas Karimun, Kabupaten Karimun, Kepulauan Riau, Indonesia

DOI:

https://doi.org/10.62411/tc.v24i3.13575

Abstract

Parit Rempak RORO Port is one of the sea transportation hubs that serves inter-regional connectivity in Karimun Regency. The limitations of the dock infrastructure, such as a length of 100 meters and a width of 11 meters, pose challenges in managing ship berthing schedules, especially RORO type ships. This study aims to analyze the suitability of the dock capacity to the ship operational schedule using the Berth Allocation Problem (BAP) approach. The BAP model is compiled and simulated through the Python-based Google Colaboratory platform to avoid scheduling conflicts and minimize ship waiting time. The simulation results are displayed in the form of a Gantt Chart which shows that the four ships: KMP Teluk Singkil 1, KMP Teluk Singkil 2, KMP Tandemand, and KMP Kundur can be scheduled sequentially without overlapping berthing times. Spatial analysis of the length and width of the dock also shows that this port is ideal for serving one ship at a time. This study shows that digitalization of the scheduling system can be an efficient solution in optimizing dock operations.   Keywords - RORO Port, Berth Allocation Problem, ship scheduling, Google Colab, dock optimization

Downloads

Published

2025-08-18

Issue

Vol. 24 No. 3 (2025): Agustus 2025

Section

Articles

License

Copyright (c) 2025 Nisha Desfi Arianti, Mey Krisselni Sitompul, Wulan Syahfina, Selvika Putri Rahmania, Ryryn Pransiska, Leonardo Zerrand

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

 

License Terms

All articles published in Techno.COM Journal are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This means:

1. Attribution

Readers and users are free to:

  • Share – Copy and redistribute the material in any medium or format.

  • Adapt – Remix, transform, and build upon the material.

As long as proper credit is given to the original work by citing the author(s) and the journal.

2. Non-Commercial Use

  • The material cannot be used for commercial purposes.

  • Commercial use includes selling the content, using it in commercial advertising, or integrating it into products/services for profit.

3. Rights of Authors

  • Authors retain copyright and grant Techno.COM Journal the right to publish the article.

  • Authors can distribute their work (e.g., in institutional repositories or personal websites) with proper acknowledgment of the journal.

4. No Additional Restrictions

  • The journal cannot apply legal terms or technological measures that restrict others from using the material in ways allowed by the license.

5. Disclaimer

  • The journal is not responsible for how the published content is used by third parties.

  • The opinions expressed in the articles are solely those of the authors.

For more details, visit the Creative Commons License Page:
? https://creativecommons.org/licenses/by-nc/4.0/

 

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.