Genetic Algorithm for Efficient Yard Crane Scheduling: A Graduation Design Project

In the graduation design, I chose the genetic algorithm to solve the multiple yard cranes scheduling, in order to understand the impact of intelligent algorithms on a specific logistics segment. Using MATLAB to optimally solve the model numerous times, I gained insight into the algorithm's impact on the model from the results of each output. I then brought the data into this genetic algorithm based on an elite retention strategy for numerical experiments. The results showed that the obtained scheduling scheme can effectively reduce the moving cost of the yard cranes and the waiting cost of the trucks. However, while there is some connection to logistics practice, many applications are still in the modeling and software simulation stage. I aspire to visit a logistics port to gain insight into the reality of cargo yards. I am determined to continue to strengthen applied research in my studies and explore practical applications of intelligent logistics and scientific and efficient management methods.