Optimizing Ship Berthing Allocation Strategies at a Dry Bulk Fertilizer Terminal
A Simulation Approach Considering Material Handling Equipment Availability
Keywords:
Demurrage Costs, Material Handling, Dry Bulk Cargo, Berth Allocation, Discrete Event SimulationAbstract
Long loading and unloading times at ports lead to demurrage costs, which occur when these activities exceed the agreed time. Demurrage is a penalty paid by shippers or charters to ship owners for delays. These delays can be caused by factors such as a lack of available docks or unprepared material handling equipment. This study examines dry bulk ports, focusing on four types of material handling, each with distinct characteristics and functions: the Continuous Ship Unloader (CSU) and Kangaroo Crane (KC) for unloading, and the Vessel Crane Flat Truck (VCFT), New Ship Unloader (NSL), and Vessel Crane with Dump Truck (VCDT) for loading. The CSU is the primary equipment for unloading, while the NSL is prioritized for loading bulk cargo. The variety of equipment and cargo adds complexity, best addressed through a discrete simulation approach. The study aims to identify the optimal berth allocation scenario, reducing loading and unloading times, waiting times, and demurrage costs using a discrete event simulation approach. Improvement scenario 2, which assigns berths based on the material being handled, resulted in a 33.49% increase in unloading revenue, a 1.81% increase in loading revenue, and a 13.54% reduction in demurrage costs.
References
[1] V. EFECAN, “An application of the DEA-cross efficiency approach in Turkish dry-bulk and general cargo terminals,” Marine Science and Technology Bulletin, vol. 12, no. 4, pp. 540–552, Dec. 2023, doi: 10.33714/masteb.1377896.
[2] S. H. Jeong, Y. S. Choi, M. Listan Bernal, and G. T. Yeo, “Analysis of obstacles to lowering demurrage at grain terminals in South Korea,” Asian Journal of Shipping and Logistics, vol. 40, no. 1, 2024, doi: 10.1016/j.ajsl.2023.12.003.
[3] Y. Keceli, “A simulation model for gate operations in multi-purpose cargo terminals,” Maritime Policy and Management, vol. 43, no. 8, 2016, doi: 10.1080/03088839.2016.1169448.
[4] A. D. de León, E. Lalla-Ruiz, B. Melián-Batista, and J. M. Moreno-Vega, “A simulation–optimization framework for enhancing robustness in bulk berth scheduling,” Engineering Applications of Artificial Intelligence, vol. 103, 2021, doi: 10.1016/j.engappai.2021.104276.
[5] I. Castilla-Rodríguez, C. Expósito-Izquierdo, B. Melián-Batista, R. M. Aguilar, and J. M. Moreno-Vega, “Simulation-optimization for the management of the transshipment operations at maritime container terminals,” Expert Systems with Applications, vol. 139, 2020, doi: 10.1016/j.eswa.2019.112852.
[6] I. Lovrić, D. Bartulović, M. Viduka, and S. Steiner, “Simulation analysis of seaport rijeka operations with established dry port,” Pomorstvo, vol. 34, no. 1, 2020, doi: 10.31217/p.34.1.15.
[7] M. M. Putri, A. Rusdiansyah, and S. Nurminarsih, “Model of twin automatic stacking crane operation strategy with dynamic handshake area in an automated container terminal,” Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri, vol. 25, no. 1, pp. 79–96, Jun. 2023, doi: 10.9744/jti.25.1.
[8] M. Neagoe, H. H. Hvolby, M. S. Taskhiri, and P. Turner, “Using discrete-event simulation to compare congestion management initiatives at a port terminal,” Simulation Modelling Practice and Theory, vol. 112, 2021, doi: 10.1016/j.simpat.2021.102362.
[9] A. H. Gharehgozli, F. G. Vernooij, and N. Zaerpour, “A simulation study of the performance of twin automated stacking cranes at a seaport container terminal,” European Journal of Operational Research, vol. 261, no. 1, 2017, doi: 10.1016/j.ejor.2017.01.037.
[10] A. Malekahmadi, M. Alinaghian, S. R. Hejazi, and M. A. Assl Saidipour, “Integrated continuous berth allocation and quay crane assignment and scheduling problem with time-dependent physical constraints in container terminals,” Computers & Industrial Engineering, vol. 147, 2020, doi: 10.1016/j.cie.2020.106672.
[11] E. Lujan, E. Vergara, J. Rodriguez-Melquiades, M. Jiménez-Carrión, C. Sabino-Escobar, and F. Gutierrez, “A fuzzy optimization model for the berth allocation problem and quay crane allocation problem(BAP + QCAP) with n quays,” Journal of Marine Science and Engineering, vol. 9, no. 2, 2021, doi: 10.3390/jmse9020152.
[12] X. Sun, S. Wang, Z. Wang, C. Liu, and Y. Yin, “A semi-automated approach to stowage planning for Ro-Ro ships,” Ocean Engineering, vol. 247, 2022, doi: 10.1016/j.oceaneng.2022.110648.
[13] J. Li, Y. Zhang, S. Y. Ji, and J. Ma, “Inland container ship stowage planning decision with multiple container types,” Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, vol. 19, no. 1, 2019, doi: 10.16097/j.cnki.1009-6744.2019.01.030.
[14] Y. Wang, G. Shi, and K. Hirayama, “Many-objective container stowage optimization based on improved NSGA-III,” Journal of Marine Science and Engineering, vol. 10, no. 4, 2022, doi: 10.3390/jmse10040517.
[15] S. C. Chang, M. H. Lin, and J. F. Tsai, “An optimization approach to berth allocation problems,” Mathematics, vol. 12, no. 5, 2024, doi: 10.3390/math12050753.
[16] B. C. Jos, M. Harimanikandan, C. Rajendran, and H. Ziegler, “Minimum cost berth allocation problem in maritime logistics: new mixed integer programming models,” Sadhana - Academy Proceedings in Engineering Sciences, vol. 44, no. 6, 2019, doi: 10.1007/s12046-019-1128-7.
[17] L. P. Prencipe and M. Marinelli, “A novel mathematical formulation for solving the dynamic and discrete berth allocation problem by using the bee colony optimisation algorithm,” Applied Intelligence, vol. 51, no. 7, 2021, doi: 10.1007/s10489-020-02062-y.
[18] A. Budipriyanto, B. Wirjodirdjo, I. N. Pujawan, and S. Gurning, “A simulation study of collaborative approach to berth allocation problem under uncertainty,” Asian Journal of Shipping and Logistics, vol. 33, no. 3, 2017, doi: 10.1016/j.ajsl.2017.09.003.
[19] J. Wawrzyniak, M. Drozdowski, and É. Sanlaville, “Selecting algorithms for large berth allocation problems,” European Journal of Operational Research, vol. 283, no. 3, 2020, doi: 10.1016/j.ejor.2019.11.055.
[20] A. Kramer, E. Lalla-Ruiz, M. Iori, and S. Voß, “Novel formulations and modeling enhancements for the dynamic berth allocation problem,” European Journal of Operational Research, vol. 278, no. 1, 2019, doi: 10.1016/j.ejor.2019.03.036.
[21] B. Martin-Iradi, D. Pacino, and S. Ropke, “The multiport berth allocation problem with speed optimization: Exact methods and a cooperative game analysis,” Transportation Science, vol. 56, no. 4, 2022, doi: 10.1287/trsc.2021.1112.
[22] E. H. Issam, A. Lajjam, M. El Merouani, and Y. Tabaa, “A modified sailfish optimizer to solve dynamic berth allocation problem in conventional container terminal,” International Journal of Industrial Engineering Computations, vol. 10, no. 4, 2019, doi: 10.5267/j.ijiec.2019.4.002.
[23] E. T. Bacalhau, L. Casacio, and A. T. de Azevedo, “New hybrid genetic algorithms to solve dynamic berth allocation problem,” Expert Systems with Applications, vol. 167, 2021, doi: 10.1016/j.eswa.2020.114198.
[24] T. Nishi, T. Okura, E. Lalla-Ruiz, and S. Voß, “A dynamic programming-based matheuristic for the dynamic berth allocation problem,” Annals of Operations Research, vol. 286, no. 1–2, 2020, doi: 10.1007/s10479-017-2715-9.
[25] M. Yu, Y. Lv, Y. Wang, and X. Ji, “Enhanced ant colony algorithm for discrete dynamic berth allocation in a case container terminal,” Journal of Marine Science and Engineering, vol. 11, no. 10, 2023, doi: 10.3390/jmse11101931.
[26] F. Barbosa, P. C. B. Rampazzo, A. T. de Azevedo, and A. Yamakami, “The impact of time windows constraints on metaheuristics implementation: A study for the discrete and dynamic berth allocation problem,” Applied Intelligence, vol. 52, no. 2, 2022, doi: 10.1007/s10489-021-02420-4.
[27] M. S. Yıldırım, M. M. Aydın, and Ü. Gökkuş, “Simulation optimization of the berth allocation in a container terminal with flexible vessel priority management,” Maritime Policy and Management, vol. 47, no. 6, 2020, doi: 10.1080/03088839.2020.1730994.
[28] X. Li, Y. Zhao, P. Cariou, and Z. Sun, “The impact of port congestion on shipping emissions in Chinese ports,” Transportation Research Part D: Transport and Environment, vol. 128, 2024, doi: 10.1016/j.trd.2024.104091.
[29] A. Dávila De León, E. Lalla-Ruiz, and B. Melián-Batista, “Disruption management approaches for berth scheduling in bulk terminals,” Journal of Advanced Transportation, vol. 2022, 2022, doi: 10.1155/2022/8069796.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Maulin Masyito Putri, Prita Meilanitasari, Nindya Putri Prasodjo, Dwi Sekar Arumjani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles published in the Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri will be Open-Access articles distributed under the terms and conditions of the Creative Commons Attribution License (CC BY).
This work is licensed under a Creative Commons Attribution License (CC BY).
