Application Of Adaptive Genetic Algorithm In The Vehicle Scheduling Problem

With the rapid development of the national economy, a dramatic increase in the demand of the social logistics, promote logistics industry to maintain sustained, steady and rapid’ development.. In the actual operation of the logistics process, the base vehicle scheduling has been are one of the key factors to affect the enterprise efficiency of the transport and logistics costs. The library is refers to the goods from the loaded into the truck began to arrive at the unloading silo door to unload, to distribution center and distribution of goods processing, to loading bin gate of the loading of the goods on the whole process of an organization, goods will not be stored in the warehouse but direct distribution. This organization implement, can from the cost, time, and other aspects of the reduced, thereby greatly enhancing the efficiency of logistics. The base vehicle scheduling problem can be described as: under the certain constraints, how to reasonably distribute the vehicle with the bin gate makes to meet the constraints of the whole operation optimization problem in the cost or time to achieve. The base vehicle scheduling problem is one of the typical NP hard (NP hard) problem, is also the most difficult to solve the classical combinatorial optimization problems.As one of the most important methods of bionics algorithm genetic algorithm, and it is also one of the most widely used evolutionary computation methods. Genetic algorithm in solve a variety of nonlinear optimization problems exhibit adaptive and global optimality and implicit parallel features let it has irreplaceable advantages in scheduling optimization. In this paper, based on the genetic algorithm, a new algorithm for solving the multi-warehouse warehouse scheduling problem is proposed, which is based on the genetic algorithm. Due to the simple genetic algorithm in the application of often appear slow convergence, poor stability and premature convergence problem, and present some improved adaptive genetic algorithm in solving process prone to local optimal solution and other defects. In this paper from the genetic algorithm process of the early genetic algorithm is easy to fall into local optimum. In the late evolutionary slow defects. From the diversity of the population, the best individual preservation strategy and crossover probability and mutation probability several aspects to improve, improve the crossover and mutation methods according to the actual problem proposed a can effectively solve the multi cabin door more library vehicle scheduling problem of adaptive genetic algorithm. The results show that the algorithm has the obvious improvement in the convergence and the stability of the algorithm, and it can achieve the expected results. Finally, the paper develops a more library vehicle scheduling system based on the model and the improved algorithm for the vehicle scheduling.