Design and Simulation of Logistics Network Model Based on Particle Swarm Optimization Algorithm

Abstract

With the continuous development of e-commerce, logistics and express services have penetrated into every aspect of people’s life. Research on the optimization of logistics network model is helpful to reduce the waste of routes, improve the utilization rate of transportation tools and hubs, and thus reduce the organizational cost of logistics. In this paper, the basic model of hub-and-spoke network (HSN) is constructed based on the principle of minimizing the connection distance and total cost between hubs. By discretizing the particles in the continuous motion space, the discrete particle swarm optimization (PSO) algorithm is designed and the Exchange () exchange function is used to improve its search strategy to update the individual optimal value and the global optimal value. Finally, the improved double-layer discrete PSO algorithm is obtained to solve the logistics network model. The results show that the optimized PSO algorithm has faster convergence speed and higher precision, and the application of the logistics network model is helpful to integrate logistics resources, reduce logistics costs, and improve logistics efficiency.

Design a Robust Logistics Network with an Artificial Physarum Swarm Algorithm

“…The DL approach [41] is set as a convolutional neural network with eight nodes in the input layer, eight nodes in every immediate layer and eight output nodes in the output layer. As we can see from Figure 7, in solving the logistics network robustness problem, the proposed APS system can obtain a solving performance similar to those of artificial intelligence algorithms, such as the ACO [36], ANN [37,38], PSO [13,39,40], GA [39] and DL [41]. When the number of logistics nodes is small, such as 30 nodes in Figure 7a and 60 nodes in Figure 7b, these artificial intelligence algorithms can obtain a similar performance, where the proposed APS can obtain satisfying computing errors.…”

“…More importantly, the problem-solving process of our proposed artificial system is greatly simplified, where the complex biological instruments and professional biological operations [14,15,42,43] have all disappeared in our method, such as the Physarum cultivation, environmental control, experimental design, biosafety and data analysis. Now, the related artificial intelligence algorithms are listed here for a comparison to solve the traffic planning problem of Mexican highways, including the ant colony optimization (ACO) [36], artificial neural network (ANN) [37,38], particle swarm optimization (PSO) [13,39,40], genetic algorithm (GA) [39] and deep learning (DL) approach [41]. The related results are shown in Figure 7a-d, where the number of network nodes is extended from 30, 60, 90 to 120.…”

“…The parameters of the ANN [37,38] are set as a three-layer BP neural network with three nodes in the input layer, two nodes in the immediate layer and three output nodes in the output layer. The PSO [13,39,40] sets the population size as N = 40, location limitation as 50, speed limitation as [−0.5, 0.5], self-learning factor as c1 = 0.5 and social learning factor as c2 = 0.5. The GA [39] sets the population size Nind = 40, chromosome length Lind = 20, crossover probability px = 0.7 and mutation probability pm = 0.01.…”

“…Due to the difficulty of solving the robustness problem of the logistics network, many researchers applied a lot of artificial intelligence algorithms to improve the network's robustness, such as the fuzzy set [11,12], ant colony optimization (ACO) [36], artificial neural network (ANN) [37,38], particle swarm optimization (PSO) [13,39,40], genetic algorithm (GA) [39] and deep learning (DL) approach [41]. In 2010, a novel Physarum method was used for the optimization of a traffic network [14].…”

Retracted: Design and Simulation of Logistics Network Model Based on Particle Swarm Optimization Algorithm

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”