Evolutionary Computation for Berth Allocation Problems: A Survey

Berth allocation problem (BAP) is to assign berthing spaces for incoming vessels while considering various constraints and objectives, which is an important optimization problem in port logistics. Evolutionary computation (EC) algorithms are a class of meta-heuristic optimization algorithms that mimic the process of natural evolution and swarm intelligence behaivors to generate and evolve potential solutions to optimization problems. Due to the advantages of strong gobal search capability and robustness, the EC algorithms have gained significant attention in many research fields. In recent years, many studies have successfully applied EC algorithms in solving BAPs and achieved encouraging performance. This paper aims to survey the existing literature on the EC algorithms for solving BAPs. First, this survey introduces two common models of BAPs, which are continuous BAP and discrete BAP. Second, this paper introduces three typical EC algorithms (including genetic algorithm, particle swarm optimization, and ant colony optimization) and analyzes the existing studies of using these EC algorithms to solve BAPs. Finally, this paper analyzes the future research directions of the EC algorithms in solving BAPs.