Nature inspired metaheuristics for optimizing problems at a container terminal

Abstract

Nowadays, maritime transport is the backbone of the international trade of goods. Therefore, seaports play a very important role in global transport. The use of containers is significantly represented in the maritime transport. Considering the increased number of container shipments in the global transport, seaport container terminals have to be adapted to a new situation and provide the best possible service of container transfer by reducing the transfer cost and the container transit time. Therefore, there is a need for optimization of the whole container transport process within the terminal. The logistic problems of the container terminals have become very complex and logistics experts cannot manually adjust the operations of terminal processes that will optimize the usage of resources. Hence, to achieve further improvements of terminal logistics, there is a need to introduce scientific methods such as metaheuristics that will enable better and optimized use of the terminal resources in an automated way. There is a large number of research papers that have successfully proposed the solutions of optimizing the container logistic problems with well-known metaheuristics inspired by the nature. However, there is a continuous emergence of new nature inspired metaheuristics today, like artificial bee colony algorithm, firefly algorithm and bat algorithm, that outperform the well-known metaheuristics considering the most popular optimization problems like travel salesman problem. Considering these results of comparing algorithms, we assume that better results of optimization of container terminal logistic problems can be achieved by introducing these new nature inspired metaheuristics. In this paper we have described and classified the main subsystems of the container terminal and its logistic problems that need to be optimized. We have also presented a review of new nature inspired metaheuristics (bee, firefly and bat algorithm) that could be used in the optimization of these problems within the terminal.