scholarly journals LRP Model and Algorithm of Expressway Logistics Network Planning Based on Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zheng Kou ◽  
Man Zhang

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.

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yuefang Sun ◽  
Kangkang Jin ◽  
Zhaozhuang Guo ◽  
Chen Zhang ◽  
Hao Wang

In recent years, with the improvement of Internet of Things (IOT) technology, a “shared” service concept has appeared in people’s life. In the limited available resources, it is of great value to study the optimal path of charging pile selection for shared cars. With the help of Internet of Things technology and through analyzing the collected data, this paper introduces three path optimization methods, the Dijkstra algorithm, heuristic algorithm A∗, and improved particle swarm optimization (PSO) algorithm; establishes relevant convergence conditions; and takes the actual path cost as the criterion to judge the optimal path. In addition, this paper studies the optimal path from the shared car to the charging pile. Through the simulation experiment, the results show that compared with the traditional optimal path algorithm, the improved particle swarm optimization algorithm has strong parallelism and better search effect for optimal path selection in the case of large number of traffic path nodes and complex paths, which fully reflects the performance advantage of the algorithm.


The study introduces novel analytical modeling of a multipath fault-tolerant routing approach where the design principle is formulated based on a bio-inspired optimization modeling of swarm optimization principles. The prime objective of this novel approach is to deal with network failures of Internet-of-Things (IoT) in a faster manner and recover the network operations as early as possible without compromising much energy. This way, the network becomes more reliable and sustainable even if any events occur that make the senor node functionally disabled and even if any types of path failures take place, regardless of energy consumption factors in IoT routing scenarios. However, the approach also capable of handling energy problems during the IoT routing scenario to a significant extent. Further, the outcome of the study shows that the fault-tolerance routing approach based on unconventional particle swarm optimization (FT-PSO) attains better results as compared to the existing baselines.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sankar Sennan ◽  
Somula Ramasubbareddy ◽  
Sathiyabhama Balasubramaniyam ◽  
Anand Nayyar ◽  
Mohamed Abouhawwash ◽  
...  

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