Automated RFID network planning with site-specific stochastic modeling and particle Swarm Optimization

2014 ◽  
Author(s):  
Antonis G. Dimitriou ◽  
Stavroula Siachalou ◽  
Aggelos Bletsas ◽  
John N. Sahalos
Keyword(s):  
Particle Swarm Optimization ◽  
Stochastic Modeling ◽  
Network Planning ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Site Specific

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
Keyword(s):  
Particle Swarm Optimization ◽  
Internet Of Things ◽  
Particle Swarm Optimization Algorithm ◽  
Network Planning ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Actual Distribution ◽  
Logistics Network ◽  
Internet Of Things Technology ◽  
The Internet Of Things

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.

scholarly journals Robust modeling and planning of radio-frequency identification network in logistics under uncertainties

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771876978 ◽  
Author(s):  
Bowei Xu ◽  
Junjun Li ◽  
Yongsheng Yang ◽  
Octavian Postolache ◽  
Huafeng Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Cost Efficiency ◽  
Network Planning ◽  
Particle Swarm ◽  
Coverage Rate ◽  
Planning Model ◽  
Swarm Optimization ◽  
Frequency Identification

To realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification network in logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and a robust particle swarm optimization is proposed. In radio-frequency identification network planning model, coverage is established by referring the probabilistic sensing model of sensor with uncertain sensing range; reading interference is calculated by concentric map–based Monte Carlo method; cost efficiency is described with the quantity of readers. In robust particle swarm optimization, a sampling method, the sampling size of which varies with iterations, is put forward to improve the robustness of robust particle swarm optimization within limited sampling size. In particular, the exploitation speed in the prophase of robust particle swarm optimization is quickened by smaller expected sampling size; the exploitation precision in the anaphase of robust particle swarm optimization is ensured by larger expected sampling size. Simulation results show that, compared with the other three methods, the planning solution obtained by this work is more conducive to enhance the coverage rate and reduce interference and cost.

Correlation between RFID Network Planning (RNP) Parameters and Particle Swarm Optimization (PSO) Solutions

2013 ◽  
Vol 465-466 ◽  
pp. 1245-1249 ◽  
Author(s):  
Azli Nawawi ◽  
Khalid Hasnan ◽  
Salleh Ahmad Bareduan
Keyword(s):  
Particle Swarm Optimization ◽  
Network Planning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Representation System ◽  
Swarm Optimization ◽  
Optimization Result ◽  
Rfid Readers

Particle Swarm Optimization (PSO) algorithm is often used for solving RFID Network Planning (RNP) problems. However, the direct correlation between RNP parameters (coordinates and power settings of RFID readers) and PSO solutions is rarely shown. This is due to the fact that most researches done in this field focus more on the development of new variants of PSO and the optimization result. For that reason, this paper tends to investigate the correlation between RNP parameters and PSO solutions. One of RNP objectives (Optimal Tag Coverage) is taken as an example. The formula of optimal tag coverage is elaborated in order to expose the allocation of RNP parameters in the formula. In addition, a representation system for embedding RNP parameters in PSO solution is explained. This paper can also serves as an early guideline for solving RNP problems using PSO algorithm.

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