Optimization Model of Logistics Task Allocation Based on Genetic Algorithm

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