Optimization of Port Train Scheduling Using Simulated Annealing Algorithm

With the increasing complexity of engineering systems, large-scale optimization problems are becoming more difficult to solve using traditional methods. This paper develops a joint scheduling model for train operations within ports and employs the Simulated Annealing Algorithm (SAA) to solve it. By leveraging its probabilistic jumping mechanism, SAA can escape local optima and converge toward a global optimum in the solution space. Experimental results show that SAA reduces the average train dwell time at the port by 15.23 units compared to a heuristic algorithm, demonstrating its effectiveness and superiority in port rail scheduling.