Optimization of railway freight wagon overhaul scheduling based on improved simulated annealing algorithm

In this paper, an assembly scheduling optimization model that considers the arrival order of key components is constructed to address two major challenges in the overhauling process of railway freight wagons: the original-car component matching principle and the uncertainty of the arrival time of key components. The model is implemented by the Improved Simulated Annealing Algorithm (ISA), which integrates a multi-neighborhood search capability, an elite solution acceptance strategy, and an adaptive temperature control mechanism. Comparison experiments with Grey Wolf Optimization (GWO) and Genetic Algorithm (GA) show that the proposed algorithm achieves optimal results in two key metrics, namely, maximum completion time and assembly line waiting time. The experimental data show that the ISA algorithm not only converges faster than the comparative algorithms but also demonstrates higher stability and adaptability, maintains the smallest range of deviation in different sizes of test instances, and effectively improves the assembly efficiency of the workshop and reduces the waste of resources caused by the waiting time. The experimental results verify the practical value of the algorithm in railway freight wagon maintenance scenarios, and provide a feasible solution for similar assembly workshop scheduling optimization problems under multiple constraints.