Research And Implementation Of Vehicle Route Optimization Based On Bionic Intelligent Algorithms

In recent years,with the rise of social intelligent transportation and the continuous development of e-commerce,China's modern logistics industry has entered a period of rapid development.The key part of the optimization of modern logistics industry is the optimization of transportation links.The optimization problem of the transportation link belongs to the vehicle routing problem(VRP),and the VRP belongs to the NP-hard combination optimization problem.Researchers have been inspired by the mechanism of biological evolution,applying phage algorithm,genetic algorithm and other bionic intelligent algorithms to solve vehicle routing problems.This paper originated from the forward-looking research of a local logistics company in Shenzhen.By adding constraints to the standard vehicle routing problem,two extended vehicle path optimization models were established: vehicle path problem with capacity constraints(CVRP)and multi-objective optimization.Vehicle Routing Problem(MOVRP),the specific work is as follows:(1)In order to solve the problem of vehicle path optimization with capacity constraints,this paper defines the indirect expectation heuristic and introduces it into the path selection probability based on the basic ant colony algorithm,using the combination of global and local update.The pheromone update of the ant colony algorithm is combined with the local search algorithm 2-opt to optimize the optimal solution of each iteration.An improved hybrid ant colony algorithm is proposed,and the algorithm is validated on two public data sets Eil51 and Lin105.Effectiveness(2)Based on the improved hybrid ant colony algorithm,this paper studies the basis of the vehicle path optimization problem with capacity constraints,and combines the characteristics of the single target optimization problem to establish a mathematical model of the vehicle path problem with capacity constraints.Single-objective optimization algorithm for vehicle routing with capacity constraints,and verified the feasibility of the algorithm in the case of VRPLIB;(3)Based on the classic fast non-dominated sorting genetic algorithm,in order to meet the logistics enterprise's requirements for the total cost of logistics and customer satisfaction,this paper designs a multi-objective optimized vehicle routing optimization algorithm,and designs and develops the enterprise application system.The algorithm is applied to the actual business data flow of the enterprise,which improves the actual business processing efficiency of the enterprise and achieves the expected goal.Based on the research of a local logistics enterprise in Shenzhen,this paper uses the ant colony algorithm and the non-dominated sorting genetic algorithm as the basic tools,the principle of combining theory with practice,and solves the problem of vehicle path with capacity constraints by using improved hybrid ant colony algorithm.The non-dominated sorting genetic algorithm solves the actual multi-objective optimization vehicle routing problem of logistics enterprises.