Dynamic and Continuous Berth Allocation using Cuckoo Search Optimization

Over the last couple of decades, demand for seaborne containerized trade has increased significantly and it is expected to continue growing over the coming years. As an important node in the maritime industry, a maritime container terminal (MCT) should be able to tackle the growing demand for sea trade. Due to the increased number of ships that can arrive simultaneously at an MCT combined with inefficient berth allocation procedures, there are often undesirable situations when the ships have to stay in waiting queues and delay both their berthing and departure. In order to improve port efficiency in terms of reducing the total handling cost and late departures, this study investigates the dynamic and continuous berth allocation problem (DC-BAP), where vessels are assigned dynamically as they arrive at their berth locations assuming a continuous berth layout. First, the DC-BAP is formulated as a mixed-integer linear programming (MILP) model. Since BAP is an NP-hard problem and cannot be solved by mathematical approaches in a reasonable time, this study adopts the recently developed metaheuristic cuckoo search algorithm (CSA) to solve the DCBAP. For validating the performance of the proposed CSA method, we use a benchmark case study and a genetic algorithm solution proposed in recent literature as well as compare our results against the optimal MILP solution. From the simulation results, it becomes evident that the newly proposed algorithm has higher efficiency over counterparts in terms of optimal berth allocation within reasonable computation time.