Optimization Model of Logistics Task Allocation Based on Genetic Algorithm

Abstract

In order to improve the efficiency of logistics task allocation, the rationality and algorithm of the logistics cloud task scheduling model based on genetic algorithm are proposed in this paper. Firstly, the basic principle of genetic algorithm is introduced, the logistics cooperative distribution model is constructed, and the judgment mathematical model of the transfer point of the logistics distribution demand point is constructed. Genetic algorithm is used to solve the logistics distribution path planning model, and the model is simplified. The complex multiobjective optimization problem is transformed into a single-objective optimization problem through preference vector. The genetic algorithm and open-source algorithm on Python are used to simulate the model proposed in this paper. From the change curve of the objective function, after 100 generations of iteration, the value of objective function increases rapidly from 30 to 130 and slowly from generation 5 to generations 40 to 130. Subsequently, the 40th generation to 60th generation were rapidly upgraded to 160. Finally, the 60th to 100th generations are basically stable at about 170. The cost in the scheduling process decreases gradually with the increase of the number of iterations of the algorithm, from the initial unit cost of nearly 200 to 120. Then it gradually decreases to about 80. Genetic algorithm shows the ability of efficient and accurate solution in this 100-generation iteration. The genetic algorithm is used to solve the problem. The algorithm parameters are as follows: population size pop size = 300, maximum number of iterations max gen = 200, crossover probability PC = 0.8, and mutation probability PM = 0.1. Using the data in this paper and substituting it into the model established in this paper, the following distribution scheme is obtained: p the minimum distribution cost is 601.58 yuan, the distribution vehicle is 5, and the total mileage is 477.41. After using the algorithm to optimize the path, the path interleaving is greatly reduced, and the vehicles do not take the repeated route, which can greatly save the cost. After calculation, the total mileage after optimization is 74.8% lower than that before optimization, and the cost is significantly reduced by 72.8%. To sum up, the last kilometer distribution algorithm proposed in this paper can greatly reduce the cost of logistics resource scheduling, which has obvious research significance.