Abstract: In military railway transportation, the pallet loading problem is described as loading a set of equipments of different sorts into pallets of some given style. The models and algorithms for pallet loading problem are presented to satisfy different demands. First Fit algorithm and conditional mutation operator are introduced into simple genetic algorithm for obtaining a better solution, and an improved genetic algorithm is proposed for solving a kind of pallet loading problem. In the improved genetic algorithm, the idea of First Fit algorithm and conditional mutation operator is used to improve the ineffective chromosome in the process of evaluation, and the improved selection operator, crossover operator and mutation operator are used to solve the problem of premature convergence of genetic algorithm. The effectiveness of the improved genetic algorithm is convinced through computational results of an example. From the viewpoint of computational results obtained, it is confirmed that the improved genetic algorithm outperforms next fit algorithm, First Fit algorithm, First Fit decreasing algorithm and simple genetic algorithm.

Key words: Pallet loading, First Fit algorithm, Genetic algorithm, Conditional mutation

摘要： 分析了铁路运输中的平车装载问题，借鉴了First Fit算法的思想，并引入条件变异算子，提出了求解平车装载问题的一种改进遗传算法，给出了该改进遗传算法编码方法、遗传算子改进方案和适应度函数的定义，该算法能有效地解决初始群体和进化过程中的无效染色体和早熟问题，并用实例验证了该算法的有效性。

关键词: 平车装载, First Fit算法, 遗传算法, 条件变异

CLC Number: 

• TP301.6