scholarly journals An optimization model for the operations of a pallet pool with both radio-frequency identification–tagged pallets and non-tagged pallets

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401774801 ◽  
Author(s):  
Jianwei Ren ◽  
Chunhua Chen ◽  
Hao Xu ◽  
Qingqing Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Radio Frequency ◽  
Optimization Algorithm ◽  
Radio Frequency Identification ◽  
Optimization Model ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Identification System ◽  
Swarm Optimization ◽  
Frequency Identification

In a pallet pool, pallets would be delivered through a supply chain. The operation procedure that consists of at least five operation processes as distribution, reposition, recycling, purchase (or rent), and maintenance is quite complex. These pallets are likely to be damaged, lost, destroyed, and so on. So, it is necessary to monitor the pallets using radio-frequency identification technology. However, there is no literature on the management of a pallet pool with both radio-frequency identification–tagged pallets and non-tagged pallets being put into consideration. In our research, an optimization model is presented to manage such a pallet pool. The objective of the optimization model is to minimize the total operation cost of a pallet pool including distribution cost, reposition cost, recycling cost, purchase or rent cost, loss cost, maintenance cost, loading and unloading cost, storage cost, and punishment cost. A particle swarm optimization algorithm is developed in Microsoft Visual Basic. Our numerical example shows that the optimization model and particle swarm optimization algorithm are effective. It is proved that the model and algorithm also can be used to measure whether the investment of a radio-frequency identification system is valuable or not. We proposed some suggestions for the pallet pools management.

scholarly journals Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm

2019 ◽  
Vol 11 (1) ◽  
pp. 542-548
Author(s):  
Wenlong Tang ◽  
Hao Cha ◽  
Min Wei ◽  
Bin Tian ◽  
Xichuang Ren
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Communication System ◽  
Particle Swarm Optimization Algorithm ◽  
Estimation Method ◽  
Particle Swarm ◽  
Automatic Identification ◽  
Identification System ◽  
Signal Power ◽  
Swarm Optimization

Abstract This paper proposes a new refractivity profile estimation method based on the use of AIS signal power and quantum-behaved particle swarm optimization (QPSO) algorithm to solve the inverse problem. Automatic identification system (AIS) is a maritime navigation safety communication system that operates in the very high frequency mobile band and was developed primarily for collision avoidance. Since AIS is a one-way communication system which does not need to consider the target echo signal, it can estimate the atmospheric refractivity profile more accurately. Estimating atmospheric refractivity profiles from AIS signal power is a complex nonlinear optimization problem, the QPSO algorithm is adopted to search for the optimal solution from various refractivity parameters, and the inversion results are compared with those of the particle swarm optimization algorithm to validate the superiority of the QPSO algorithm. In order to test the anti-noise ability of the QPSO algorithm, the synthetic AIS signal power with different Gaussian noise levels is utilized to invert the surface-based duct. Simulation results indicate that the QPSO algorithm can invert the surface-based duct using AIS signal power accurately, which verify the feasibility of the new atmospheric refractivity estimation method based on the automatic identification system.

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.

scholarly journals Path Optimization Model for Urban Transportation Networks under the Perspective of Environmental Pollution Protection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Can Liu ◽  
Zongping Li ◽  
Yueyang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Environmental Pollution ◽  
Real Time ◽  
Optimization Algorithm ◽  
Optimization Model ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Path Optimization ◽  
Swarm Optimization ◽  
Real Time Traffic

In view of the lack of consideration of environmental protection performance in the traditional path optimization model, a path optimization model for urban traffic networks from the perspective of environmental pollution protection is proposed. Firstly, the urban real-time traffic condition is expressed by the road traffic state index, and an integer programming model is established to optimize the route with the goal of low carbon and shortest distribution time. Then, a hybrid particle swarm optimization algorithm combined with adaptive disturbance mechanism based on variable neighborhood descent is designed, which can better carry out adaptive disturbance according to the situation that the population falls into local extreme value, and the 2-opt local search method is introduced to improve the quality of solution. Finally, the improved particle swarm optimization algorithm is used to solve the two-objective model to obtain the Pareto front solution set, that is, the path scheme under real-time traffic conditions. The experimental demonstration of the proposed model based on two application scenarios shows that its distribution cost, distribution time, and carbon emission are 1975 yuan, 27 h, and 213 kg, respectively, which are better than other comparison models and have high application value.

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