Optimization of Container Dwell Time at Tanjung Perak Port, Indonesia: Container Flow Approach
DOI:
https://doi.org/10.70716/ecoma.v4i2.444Keywords:
Container, Dwell Time, Optimization, Tanjung PerakAbstract
Dwell Time (DT) is the waiting period required for an imported container, measured from the time the container is unloaded from a ship until it leaves the port area. Tanjung Perak Port, Indonesia's second-largest logistics hub, recorded a DT of 3.36 days in July 2025, exceeding the national target of 2.87 days. In July 2025, Tanjung Perak Port handled 53,446 import containers, representing 20.98% of the total import containers entering Indonesia's five main ports. This study aims to determine the optimal DT point at Tanjung Perak Port using quantitative econometric methods, specifically quadratic models and optimization. The study uses secondary data from January to September 2025, comprising 397,485 containers, which were analyzed using Stata 19 and Excel Solver. The results indicate that the minimum optimal DT at Tanjung Perak Port is 2.48 days. The time components consist of 1.24 days from stacking to job order (X1), 0.96 days from job order to gate-in (X2), and 0.28 days from truck gate-in to gate-out (X3). These findings suggest that the DT at Tanjung Perak Port can be further optimized to reach its minimum level. This study uses container flow as a proxy for goods movement; therefore, future research may incorporate both goods and document flows to provide a more comprehensive analysis.
Downloads
References
Arvis, J.-F., Ojala, L., Shepherd, B., Ulybina, D., & Wiederer, C. (2023). Connecting to compete 2023: Trade logistics in an uncertain global economy. World Bank.
Chhetri, P., Nguyen, S., Gekara, V., & Sharma, S. (2025). Container dwell time predictive modeling: An application of ML algorithms. Maritime Policy & Management, 1–31. https://doi.org/10.1080/03088839.2025.2501010
Chiang, A. C., & Wainwright, K. (2005). Fundamental methods of mathematical economics (4th ed.). McGraw-Hill.
Ewamer, A. G., & Menyhárt, J. (2022). Long container dwell time at seaport terminals: An investigation study from a consignee perspective. International Journal of Engineering and Management Sciences, 7(1), 106-120. https://doi.org/10.21791/IJEMS.2022.1.9
Feng, Y., Song, D.-P., & Li, D. (2022). Smart stacking for import containers using customer information at automated container terminals. European Journal of Operational Research, 301(2), 502-522. https://doi.org/10.1016/j.ejor.2021.10.044
Hassan, R., Gurning, R. O., & Handani, D. W. (2020). Analysis of the container dwell time at container terminal by using simulation modelling. International Journal of Marine Engineering Innovation and Research, 4(4), 34–43. https://doi.org/10.12962/J25481479.V4I4.5711
Jahangard, M., Xie, Y., & Feng, Y. (2025). Leveraging machine learning and optimization models for enhanced seaport efficiency. Maritime Economics & Logistics, 27, 710-751. https://doi.org/10.1057/s41278-024-00309-w
Lee, Y., Park, K., Lee, H., Son, J., & Kim, S. (2024). Identifying key factors influencing import container dwell time using explainable artificial intelligence. Maritime Transport Research. https://doi.org/10.1016/j.martra.2024.100116
LNSW. (2025a). Bahan paparan LNSW. LNSW.
LNSW. (2025b). Laporan analisis dwelling time periode Juli 2025. LNSW.
Majengo, P., & Mwendapole, M. J. (2025). An empirical assessment of container dwell-time changes at Dar es Salaam Port before and after privatization. Social Science and Humanities Journal, 9(9), 9084–9094. https://doi.org/10.18535/sshj.v9i09.1970
Maldonado, S., González-Ramírez, R. G., Quijada, F., & Ramírez-Nafarrate, A. (2019). Analytics meets port logistics: A decision support system for container stacking operations. Decision Support Systems, 121, 84-93. https://doi.org/10.1016/j.dss.2019.04.006
Pelindo. (2025). Data kontainer impor melalui Pelabuhan Tanjung Perak Surabaya. Pelindo.
Raballand, G., Refas, S., Beuran, M., & Isik, G. (2012). Why does cargo spend weeks in Sub-Saharan African ports? Lessons from six countries. World Bank.
Rachman, A., Rahayu, S., Sambarani, B., Sugianto, D., & Kwartama, A. (2022). Supply chain performance of the logistics business using the SCOR model in Tg. Priok Port Jakarta. IOP Conference Series: Earth and Environmental Science, 1081(1), Article 012059. https://doi.org/10.1088/1755-1315/1081/1/012059
Rodrigues, T. D., Mota, C. M., Ojiako, U., Chipulu, M., Dweiri, F., & Marshall, A. (2024). Competitiveness throughout the seaport-hinterland: A container shipping analysis. Maritime Policy & Management, 51(6), 1170–1189. https://doi.org/10.1080/03088839.2023.2248125
Saini, M., & Lerher, T. (2024). Assessing the factors impacting shipping container dwell time: A multi-port optimization study. Business: Theory and Practice, 25(1), 51-60. https://doi.org/10.3846/btp.2024.19205
Shiraishi, D., Zhang, W., Shibasaki, R., & Elhan-Kayalar, Y. (2026). Examining container terminal efficiency with diverse data sources: Vessel, truck, and container turnaround times in Japanese terminals. Logistics, 10(2), Article 51. https://doi.org/10.3390/logistics10020051
Sidik, A. D., Ramdani, D., Sopandita, D., Fadilah, A. Z., & Efendi, E. (2020). Modeling and optimization of container dwell time at Tanjung Perak Port, Indonesia. In 2020, the 6th International Conference on Computing Engineering and Design (ICCED) (pp. 1–4). IEEE. https://doi.org/10.1109/ICCED51276.2020.9415805
Triola, M. F. (2019). Essentials of statistics (6th ed.). Pearson.
Weerasinghe, B. A., Perera, H. N., & Bai, X. (2024). Optimizing container terminal operations: A systematic review of operations research applications. Maritime Economics & Logistics, 26, 307–341. https://doi.org/10.1057/s41278-023-00254-0
World Bank. (2026, March 29). Logistics Performance Index (LPI). https://lpi.worldbank.org
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Aditya Subur Purwana, Muhammad Anshar Syamsuddin, Ilham Majid

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








