Berth Allocation Considering Multiple Quays: A Practical Approach Using Cuckoo Search Optimization

布谷鸟搜索 计算机科学 元启发式 运筹学 粒子群优化 整数规划 端口(电路理论) 线性规划 数学优化 遗传算法 海洋工程 工程类 算法 数学 电气工程 机器学习
作者
Sheraz Aslam,Michalis P. Michaelides,Herodotos Herodotou
出处
期刊:Journal of Marine Science and Engineering [Multidisciplinary Digital Publishing Institute]
卷期号:11 (7): 1280-1280 被引量:9
标识
DOI:10.3390/jmse11071280
摘要

Maritime container terminals (MCTs) play a fundamental role in international maritime trade, handling inbound, outbound, and transshipped containers. 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) concerns allocating berthing positions to arriving ships to reduce total service cost, waiting times, and delays in vessels’ departures. In this work, we extend the study of continuous 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. In this study, we address MQ-BAP with the objective of minimizing the total service cost, which includes minimizing the waiting times and delays in the departure of ships. MQ-BAP is first formulated as a mixed-integer linear problem and then solved using the cuckoo search algorithm (CSA), a computational intelligence (CI)-based approach. In addition, the exact mixed-integer linear programming (MILP) method, two other state-of-the-art metaheuristic approaches, namely the genetic algorithm (GA) and particle swarm optimization (PSO), as well as a first come first serve (FCFS) approach, are also implemented for comparison purposes. Several experiments are conducted using both randomly generated and 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 achieves the best overall results in affordable time as compared to the other CI-based methods, for all considered scenarios.
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