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 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.

An Efficient Degradation Particle Swarm Optimization Algorithm for 3D Location in RFID System

2014 ◽  
Vol 1039 ◽  
pp. 544-551
Author(s):  
Jie Lin Li ◽  
Jin Fei Liu ◽  
Wei An Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Radio Frequency Identification ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Rfid Systems ◽  
Acceptable Deviation ◽  
Frequency Identification ◽  
Actual Target ◽  
Production Demand

With the proliferation of radio frequency identification (RFID) systems, existing two dimensional (2-D) location algorithms cannot meet the manufacturing demand anymore. In this paper, an efficient degradation particle swarm optimization (DPSO) algorithm is proposed to solve the three dimensional (3-D) location problems in passive tag RFID systems. Performance evaluation shows this method can approach the actual target tag position with acceptable deviation and stability which can meet the newly generated production demand.

scholarly journals TRACKING AND MONITORING OF TAGGED OBJECTS EMPLOYING PARTICLE SWARM OPTIMIZATION ALGORITHM IN A DEPARTMENTAL STORE

2011 ◽  
Vol 12 (1) ◽  
pp. 1-12
Author(s):  
Indrajit Bhattacharya
Keyword(s):  
Particle Swarm Optimization ◽  
Radio Frequency Identification ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Automation System ◽  
Swarm Optimization ◽  
Periodic Monitoring ◽  
Frequency Identification ◽  
Passive Rfid

The present paper proposes a departmental store automation system based on Radio Frequency Identification (RFID) technology and Particle Swarm Optimization (PSO) algorithm. The items in the departmental store spanned over different sections and in multiple floors, are tagged with passive RFID tags. The floor is divided into number of zones depending on different types of items that are placed in their respective racks. Each of the zones is placed with one RFID reader, which constantly monitors the items in their zone and periodically sends that information to the application. The problem of systematic periodic monitoring of the store is addressed in this application so that the locations, distributions and demands of every item in the store can be invigilated with intelligence. The proposed application is successfully demonstrated on a simulated case study.

Application of Particle Swarm Optimization-Based Digital Beamforming Technique to the Identification of Multiple SAW Tags

2008 ◽  
Vol 381-382 ◽  
pp. 443-446
Author(s):  
Hua Zhu ◽  
Q. Li ◽  
W. Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Radio Frequency Identification ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Digital Beamforming ◽  
Optimal Estimates ◽  
Frequency Identification ◽  
Good Identification ◽  
Beamforming Technique

Some tags in the radio frequency identification (RFID) application based on the surface acoustic wave (SAW) technique often locate in the field of a transceiver simultaneously, leading to difficult identification. In this paper, the digital beamforming technique is applied to identify multiple SAW ID-tags to address difficulties in identification. The direction of arrivals (DOAs) are used to denote the locations of the tags and the particle swarm optimization (PSO) algorithm is suggested to find the optimal estimates of the DOAs. Once the DOAs are obtained, the array weights are then formed and the signals of tags are recovered to implement decoding. The experiment results show that a good identification of multiple SAW ID-tags is achieved.

A trajectory planning model of tiltrotor considering multi-phase and multi-mode flight

2019 ◽  
Vol 233 (16) ◽  
pp. 6019-6031
Author(s):  
Jing Zhang ◽  
Wu Yu ◽  
Xiangju Qu
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Planning ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Model Simulation ◽  
Particle Swarm ◽  
Planning Model ◽  
Swarm Optimization ◽  
Multi Phase ◽  
Multi Mode

A trajectory planning model of tiltrotor with multi-phase and multi-mode flight is proposed in this paper. The model is developed to obtain the trajectory of tiltrotor with consideration of flight mission and environment. In the established model, the flight mission from take-off to landing is composed of several phases which are related to the flight modes. On the basis of the flight phases and the flight modes, the trajectory planning model of tiltrotor is described from three aspects: i.e. tiltrotor dynamics including motion equations and maneuverability, flight mission requirements, and flight environment including different no-fly zones. Then, particle swarm optimization algorithm is applied to generate the trajectory of tiltrotor online. The strategy of receding horizon optimization is adopted, and the control inputs in the next few seconds are optimized by particle swarm optimization algorithm. Flight mission simulations with different situations are carried out to verify the rationality and validity of the proposed trajectory planning model. Simulation results demonstrate that the tiltrotor flying with multi-mode can reach the target in three cases and can avoid both static and dynamic obstacles.

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