## 7 Conclusions and Future Research Extensions  
Maritime transportation has been a primary transportation mode, supporting the international trade between different continents since ancient times. Nowadays, maritime transportation continues to play a very important role for the economy of many countries. Taking into consideration a rapid growth of waterborne trade, marine container terminal operators must focus on upgrading the existing  
Figure 14: The population fitness STD values for the developed solution algorithms and problem instances 55–60. terminal infrastructure and improving operations planning. Efficient seaside operations are critical for marine container terminals, as disruptions in the seaside operations may result in substantial vessel service delays. This study focused on enhancing the seaside operations at marine container terminals, aiming to assist terminal operators with the design of efficient berth schedules. The berth scheduling problem was formulated as a mixed integer linear programming model, where the total weighted vessel turnaround time and the total weighted vessel late departures were minimized. The weight of vessels was introduced in the model to account for their service priority. A novel self-adaptive Evolutionary Algorithm was developed to solve the berth scheduling problem. The crossover and mutation probabilities were encoded in the chromosomes within the proposed algorithm and evolved throughout the algorithmic run. Extensive numerical experiments were undertaken to evaluate performance of the developed solution algorithm against the alternative Evolutionary Algorithms, which relied on the deterministic parameter control, adaptive parameter control, and parameter tuning strategies, respectively. It was found that all the considered solution algorithms demonstrated stability (i.e., relatively low variability) in terms of the objective function values at termination from one replication to another. Furthermore, the solution algorithms were able to maintain the adequate population diversity. Due to complexity of the proposed mathematical model, the considered solution algorithms showed better performance as compared to the exact optimization algorithm in terms of both objective function and computational time values for the small size problem instances. However, the developed self-adaptive Evolutionary Algorithm outperformed the Evolutionary Algorithms with adaptive parameter control, deterministic parameter control, and parameter tuning strategies in terms of the objective function values at termination on average by 4.01%, 6.83%, and 11.84%, respectively, over the generated large size problem instances. The computational time of the self-adaptive Evolutionary Algorithm did not exceed 64.96 s, which can be considered to be acceptable from the practical standpoint. Superiority of the self-adaptive Evolutionary Algorithm over the other algorithms can be explained by the fact that the crossover and mutation probabilities are encoded in the chromosomes and evolve throughout the algorithmic run, which further allows more efficient exploration and exploitation of the search space. Therefore, the proposed solution algorithm can serve as an efficient decision support tool for the marine container terminal operators and assist with the design of cost-effective berth schedules.