Optimizing maritime freight sustainability through berth allocation and quay crane assignment

Purpose This research paper seeks to present a holistic, analytics-driven strategy for making integrated planning decisions regarding the Berth Allocation Problem (BAP) and the Quay Crane Assignment Problem (QCAP) while simultaneously curbing carbon emissions. Design/methodology/approach The study developed a mathematical model aimed at reducing costs associated with vessel waiting times at anchorage, handling operations and carbon emissions from vessels and quay cranes. To achieve this, two approaches have been employed, namely: Gurobi and Elite Genetic Algorithms. Findings The performed numerical study highlighted the optimization solver GUROBI excelled in achieving optimal values with higher computational time, while Elite GA demonstrated superior computational efficiency, providing near-optimal solutions. Notably, Elite Genetic Algorithms offer near-optimal solutions with considerably reduced computation times compared to the Exact Solution Approach, highlighting the trade-off between solution quality and computational efficiency. Research limitations/implications The current study centred around berth allocation and quay crane allocation, while further studies can include other complex maritime processes like storage allocation and yard crane scheduling. Practical implications Managers could benefit from utilizing Elite GA as an efficient optimization technique to achieve cost-effective solutions for berth and quay crane allocation problems while ensuring reasonable computational times. Originality/value This work examines in detail the complexities of the berth allocation problem and the quay crane assignment problem, the main objectives of which are to minimise the time of a ship’s return to the port, known as turnaround time. Additionally, it aims to reduce emissions generated by quay cranes and ships while they remain idle.