Research On The Aircraft Loading Optimization Problem With Gravity Center Considered

Currently,air freight is the fastest way of transportation,but its expense is higher than others.Not only the scientific cargo loading scheme can make full use of the aircraft's weight and cargo space,reduce the cost of air transportation,but also ensure the safety of the aircraft's flight.Therefore,it is valuable to study on aircraft loading problem in theory and engineering.Air transport tasks are mainly divided into two types:(1)Cargoes are delivered to same destination;(2)Cargoes are delivered to different destinations,some of which will be unloaded or airdropped halfway.The main conatraints of former task are loading efficiency and the static stability of the gravity center.Based on the constraints of the former task,the dynamic stability of gravity center is added to the latter task.In this thesis,the MIP models are established to model the two aircraft loading problems.According to the scale and type of the problem,the exact solution and meta-heuristic solution methods are respectively used to solve the problem,and a corresponding engineering software system prototype is established.Firstly,established a loading optimization model with constant gravity center for transportation task type(1),the model aims at comprehensive optimization of load utilization and capacity utilization,the constraints include the gravity center constraint,load constraint,volume constraint,boundary constraint,non-overlap constraint,non-vacancy constraint and the placement method of cargo.Secondly,two sub-models are established for transportation task type(2).The illuminating combined model is used to model the stability of dynamic gravity center before and after cargoes unloading and the stability of static gravity center of the non-unloaded cargoes.Submodel 1 models the problem of cargoes unloading midway,minimizing the space occupied by unloading cargoes in midway;submodel 2 aims at maximizing load utilization and capacity utilization of cargo bay with the constraint of stability of dynamic gravity center before and after partial cargoes unloading.Thirdly,based on the two models of two types of air transport tasks,related solving methods are designed.According to the complexity of the problems,accurate and heuristic solution are used to solve the models.The heuristic and meta-heuristic methods are used to solve the first type of air transport problem model.The submodel 1 of the second type of air transport problem is solved by an accurate solution method,and the heuristic and meta-heuristic methods are applied to the submodel 2.The heuristic algorithm designed in this thesis is dynamic three-dimensional space division strategy based on trigeminal tree to realize the loading and positioning of the cargoes,and to improve the capacity utilization of the cargo bay with space merging strategy,and the improved simulated annealing algorithm id adopted to seek the optimization of capacity utilization.In the improved simulated annealing algorithm of this thesis,the segmented temperature-cooling function is specially designed to effectively improve the convergence speed.The method of partheno crossover in genetic algorithm combined with multi-particle optimization and perturbation strategy can effectively improve the quality of the solution.The solution of the algorithm includes the loading position,loading posture and loading sequence of the cargoes.Fourthly,in order to test the rationality of the algorithm,trials calculation is conducted on two types of problems based on data in the literature within five years.The model of type(1)is solved by the algorithm in this thesis,the results shows that the load utilization and comprehensive utilization are 99%and 96%respectively,both are higher than those in the literature.As for the second type problem,two examples with different quantities of cargoes to be unloaded are solved in this thesis,and good results are obtained respectively.Finally,an aircraft loading optimization support system is designed and developed by Python and PyQt5 in this thesis.This system is suitable for aircraft loading optimization problem and can be applied to container loading problems and vehicle loading problems.Users can set algorithm parameters and input cargo bay information.This system can read data file of cargoes,automatically identify problem types and select corresponding algorithms.After the system completed,it is tested with additional public data.The solution to the second type of problem shows that the capacity utilization,load utilization and comprehensive utilization of cargo bay reach 90%,99%and 94.5%respectively.