Elitist Strategy Genetic Algorithm-Based Planning Optimization Deriving for Smart Port Decision Support System

The demand for maritime transportation has significantly increased over the past 20 years due to the rapid pace of globalization. With the increase in freight transport, terminal managers confront the challenge of establishing the appropriate seaside operations. Although the recent development in smart ports derived from emerging technologies such as the Internet of thing (IoT), machine learning (ML), data mining, etc. could enhance efficiency in port operations. These technologies enabled the implementation of a digital-twining port which allows port managers to simulate and predict the performance of operation schedules in real-time or near real-time. In fact, one of the key technologies behind them is the decision support system (DSS), which is derived from intelligent algorithms to effectively model and optimize port operations. The DSS system, which aims to assist port planners in the port operation planning phase, in practice, this system should be computationally efficient and friendly to use. In this study, we present a decision support system to assist the port planners in berth allocation and quay crane assignment problem (BAP+QCAP), this leads to a complex combinational problem and is known as NP-hard. Therefore, we adopted a metaheuristic approach to solve the problem in a reasonable time, called an elitist strategy genetic algorithm (ESGA). To verify our approach, a comparative study is made on a large random instance. It is found that the ESGA outperformed the well-known metaheuristic algorithms both in terms of solution quality and computational efficiency.