Optimization berth allocation in container terminals: A Pyomo and Google Colab approach

Efficient berth allocation profoundly influences container terminal operations, affecting vessel waiting and turnaround times, and overall performance. This study presents a mixed-integer linear programming (MILP) model addressing the Berth Allocation Problem (BAP) in a Malaysian container port. By...

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Bibliographic Details
Published in:Ocean & coastal management 2024-11, Vol.258, p.107359, Article 107359
Main Authors: Nazri, Siti Nur 'Ain, Mokhtar, Kasypi, Abu Bakar, Anuar, Mclellan, Benjamin Craig, Mhd Ruslan, Siti Marsila
Format: Article
Language:English
Subjects:
Berth allocation problem
Berth template
Optimization
Pyomo
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Summary:Efficient berth allocation profoundly influences container terminal operations, affecting vessel waiting and turnaround times, and overall performance. This study presents a mixed-integer linear programming (MILP) model addressing the Berth Allocation Problem (BAP) in a Malaysian container port. By incorporating the Pyomo optimization library and CBC (Coin-or Branch and Cut) solver in Google Colab, optimal berth allocations are determined, minimizing vessel turnaround times. Visualized in a Space-Time diagram, the results highlight efficient allocation strategies. Despite limitations, the study optimally resolved three instances, achieving a remarkable 38.54% reduction in overall vessel turnaround time compared to FCFS (First-Come-First-Serve) allocation. By prioritizing port turnaround time, the optimization substantially reduced berthing and departure delays, aligning with UNCTAD's call for enhanced port efficiency and accelerated decarbonization efforts.
ISSN:0964-5691
DOI:10.1016/j.ocecoaman.2024.107359