Nearest-Better Clustering-Based Memetic Algorithm for Berth Allocation and Crane Assignment Problem - Jiawei Wu (IEEE Access)
The growth of international trade has accelerated the development of waterway transportation, thereby increasing the demand for the construction of container terminals. Optimizing the Berth Allocation and Crane Assignment Problem (BACAP) is a critical aspect of this process. In this paper, we investigate the capability of differential evolution (DE) algorithms in solving BACAP by modeling berth allocation as a continuous optimization problem. We first analyze an efficient clustering algorithm, Nearest-Better Clustering (NBC), and its effectiveness in partitioning candidate solutions for BACAP into multiple sub-populations. Subsequently, within each sub-population, we propose a novel memetic algorithm (MA) that utilizes the Adaptive Differential Evolution with Optional External Archive (JADE) as a global optimizer, combined with a Neighborhood Search (NS) to enhance the convergence capability of the sub-populations. Finally, to improve search efficiency, we introduce a berth offset distance as a penalty mechanism to minimize berth space wastage. In the experimental section, we conduct experiments on 15 cases and compare the results with four existing algorithms. The experimental results demonstrate that the proposed MA-NBC exhibits superior competitiveness and performance in solving BACAP.