Abstract
With the continuous improvement of the expressway logistics network, the location-routing problems (LRP) have become the obstacle to be overcome in the development of related industries. Based on the needs of modernization, in the era of the Internet of Things, classic traffic path planning algorithms can no longer meet the increasingly diverse needs, and related research results are not ideal. To reduce logistics costs and meet customer needs, this paper studies transportation route planning models and algorithms based on Internet of Things technology and particle swarm optimization. Firstly, the LRP model of expressway logistics network planning analyzes the achievement of goals, lists the assumptions, and builds the LRP model of expressway logistics network planning based on the mathematical model of path planning. Then the model is optimized and solved based on the particle swarm optimization algorithm. In order to verify the effectiveness and feasibility of the algorithm, MATLAB is used to simulate the algorithm. Finally, the LRP particle swarm optimization model of highway logistics network planning is put into the actual distribution work of a logistics company to analyze the change of distribution cost and investigate the related satisfaction. Experimental data show that the improved particle swarm optimization algorithm in this paper begins to converge in the 100th generation, the shortest running time is 57s, and the value of the objective function fluctuates slightly around 880. This shows that the model algorithm in this paper has strong search ability and stability. In the simulation experiment, the optimal objective function value of the model is 1001 yuan, which can be used to formulate the optimal distribution scheme. In the actual distribution work, the total cost of distribution before and after the application of the model was 12176.99 yuan and 9978.4 yuan, the fuel consumption cost decreased by 2097.23 yuan, and the penalty cost decreased by 85%. In the satisfaction survey, the satisfaction of all people was 9 points or above, with an average score of 9.42 points. This shows that the LRP particle swarm optimization model of expressway logistics network planning based on the Internet of Things technology can effectively save distribution costs and improve satisfaction.
Highlights
In order to verify the effectiveness and feasibility of particle swarm optimization algorithm in location-routing problems (LRP) model of highway logistics network planning, MATLAB is used to carry out simulation experiments
The improved particle swarm optimization algorithm began to converge in the 100th generation, the genetic algorithm began to converge in the 150th generation, and the standard particle swarm optimization algorithm began to converge in the 160th generation. is shows that the improved particle swarm optimization algorithm is better than genetic algorithm and standard particle swarm optimization algorithm in convergence speed
En, the maximum iteration number of the program is set to 1500 generations, and the three algorithms are run 10 times respectively. e running results of LRP model are as follows
Summary
Combining the Internet of ings technology and the formed highway network, developing logistics network and solving the problem of transportation path planning is a new idea, which can better meet the increasingly diversified logistics needs. E model considered the system operation cost and the risk of local residents and introduced a compensation factor into the risk objective function [6] After that, he coded and calculated in LINGO optimization solver and used the augmented ε-constraint method to generate Pareto optimal curve of multiobjective optimization problem. In order to improve the efficiency of logistics distribution, reduce costs, and better meet the needs of customers, this paper combines the Internet of ings technology and the formed highway network to solve the complex transportation path planning problem. Internet of Things Technology and LRP Model of Expressway Logistics Network Planning
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