Computational Intelligence Approaches for Optimizing Seaside Operations in Smart Ports
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 marine container terminal (MCT) should be able to tackle the growing demand for international sea trade. The increasing number of ships and containers creates several challenges to MCTs, such as congestion, long waiting times before ships dock, delayed departures, and high service costs. The berth allocation problem (BAP) and the quay crane assignment problem (QCAP) are two of the most important optimization problems in container terminals at ports worldwide. A BAP concerns allocating berthing positions to arriving ships to reduce total service cost, waiting times, and delays in vessels’ departures. The latter concerns assigning optimal number of quay cranes to docked vessels. From both the port operator’s and the shipping lines’ point of view, minimizing the time a vessel spends at berth and minimizing the total cost of berth operations are considered fundamental objectives with respect to terminal operations.
This project develops several computational intelligence (CI) based methodologies for several BAP formulations in real world environments with several practical constraints. The first formulation considers the stand-alone BAP with the objective of reducing the total service cost, which includes waiting cost, handling cost, and several penalties, such as a penalty for late departure and a penalty for non-optimal berth allocation. We extend the study of BAP, which considers a single quay (straight line) for berthing ships, to multiple quays, as found in many ports around the globe. Multi-quay BAP (MQ-BAP) adds the additional dimension of assigning a preferred quay to each arriving ship, rather than just specifying the berthing position and time. Eventually, this project investigates, for the first time, multi-quay combined BAP and QCAP, and solves it using CI approaches.
For all formulations, a mathematical model is developed and each problem is formulated as a mixed-integer linear programming (MILP) model based on a real port scenario and real constraints. Since BAP (and its variations) is an NP-hard problem and cannot be solved by exact optimization methods in a reasonable time, a metaheuristic approach, namely, a cuckoo search algorithm (CSA), is proposed to solve the BAP. To validate the performance of the proposed CSA-based method, we use two benchmark CI approaches, namely, the genetic algorithm (GA) and particle swarm optimization (PSO).
Several experiments are conducted using real data from the Port of Limassol, Cyprus, which has five quays serving commercial vessel traffic. The comparative analysis and experimental results show that the CSA-based method outperforms the other CI-based methods, while achieving near-optimal results in affordable time for all considered scenarios. Therefore, the proposed CI-based methods can serve as promising decision support tools and assist terminal operators while developing berth allocation plans. The latter formulation (MQ combined BAP and QCAP) will also assist port operators with the development of a fully-specified berth schedule, for container ships as well as for other general cargo or passengers ships, to ensure that the ships will be moored and departed in a timely manner.