民航货机装载优化准确建模仿真研究

Research on Loading, Optimization and Accurate Modeling and Simulation of Civil Aviation Cargo Aircraft

研究民航飞机运输装载问题,可以提高飞机装载效率,节省燃油,从而为航空公司大大节约成本。民航货机装载问题属于NP-hard问题,加之复杂的约束条件,如飞机货舱的重心包线限制、重量限制、载荷限制和结构限制等,在建模和算法选择上都有一定难度。为解决上述问题,综合考虑现实约束条件,建立了双目标混合整数规划模型,提出一种改进的遗传算法。该算法将传统遗传算法中二进制编码改进为实数编码方式,适应度函数为主目标函数载重量最大,将重心最小这一目标转化为约束条件,采用罚函数法处理模型中约束条件,遗传算子中交叉和变异方式分别设计为均匀交叉和均匀变异,以此增加种群的多样性,选择最优个体。仿真结果表明,改进的遗传算法能够快速收敛于最优解,验证了模型的可行性和算法的有效性。

Research on civil aircraft loading and transport problems can improve aircraft loading efficiency,save fuel,and greatly save cost for airlines.The NPV loading is a NP-hard problem.Combined with complicated constraints,such as the center of gravity envelope,the weight limit,the load limit and the structure limit of the aircraft cargo hold,there are some difficulties in modeling and algorithm selection.In order to solve the above problems,a bi-objective mixed integer programming model was established and a modified genetic algorithm was proposed to improves binary coding in traditional genetic algorithm to real number coding.The fitness function is the main objective function with the largest load carrying capacity.The goal of minimizing the center of gravity was transformed into the constraint conditions.The penalty function method was used to deal with the constraint conditions in the model.The genetic operator the crossover and mutation patterns were designed as uniform crossover and uniform variation,respectively,to increase the diversity of the population and select the best individual.The simulation results show that the improved genetic algorithm can quickly converge to the optimal solution and verify the feasibility of the model and the effectiveness of the algorithm.