Air Material Order Optimization Strategy Based on Aircraft Availability

2014 ◽  
Vol 926-930 ◽  
pp. 739-742
Author(s):  
Ya Qun He ◽  
Ji Jun Li ◽  
Hong Yang Guo ◽  
Hui Gao ◽  
Tao Wang
Keyword(s):  
Optimization Model ◽  
Optimization Strategy ◽  
Marginal Analysis ◽  
Model Derivation

To solve the problem of air material order problems of the station, this paper established under funding constraints to aircraft availability as a measure of the air material order optimization model, derivation the method of marginal analysis to solve the model, and use examples to illustrate the effectiveness and practicality of the method.

scholarly journals An Integration Optimization Strategy of Line Voltage Cascaded Quasi-Z-Source Inverter Parameters Based on GRA-FA

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4391
Author(s):  
Zhiyong Li ◽  
Shiping Pu ◽  
Yougen Chen ◽  
Renyong Wei
Keyword(s):  
Optimization Model ◽  
Power Loss ◽  
Optimal Solution ◽  
Loss Ratio ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Line Voltage ◽  
Multi Objective ◽  
Double Frequency ◽  
Voltage Ripple

Setting reasonable circuit parameters is an important way to improve the quality of inverters, including waveform quality and power loss. In this paper, a circuit system of line voltage cascaded quasi-Z-source inverter (LVC-qZSI) is built. On this basis, the double frequency voltage ripple ratio and power loss ratio are selected as optimization targets to establish a multi-objective optimization model of LVC-qZSI parameters. To simplify the calculation, an integration optimization strategy of LVC-qZSI parameters based on GRA-FA is proposed. Where, the grey relation analysis (GRA) is used to simplify the multi-objective optimization model. In GRA, the main influence factors are selected as optimization variables by considering the preference coefficient. Then, firefly algorithm (FA) is used to obtain the optimal solution of the multi-objective optimization model. In FA, the weights of objective functions are assigned based on the principle of information entropy. The analysis results are verified by simulation. Research results indicate that the optimization strategy can effectively reduce the double frequency voltage ripple ratio and power loss ratio. Therefore, the strategy proposed in this paper has a superior ability to optimize the parameters of LVC-qZSI, which is of great significance to the initial values setting.

A Model for Seat Allocation of Railway Passenger Transport Based on OD’s Priority

2014 ◽  
Vol 989-994 ◽  
pp. 2344-2348 ◽  
Author(s):  
Ying Ting Zhu ◽  
Fu Zhang Wang ◽  
Xing Hua Shan ◽  
Xiao Yan Lv
Keyword(s):  
Revenue Management ◽  
Inventory Control ◽  
Optimization Model ◽  
Control Method ◽  
Optimization Strategy ◽  
Passenger Transport ◽  
Railway Passenger Transport ◽  
Seat Allocation ◽  
Railway Passenger ◽  
Seat Inventory Control

Based on the seat inventory control method of revenue management in airline, the author researches the optimization strategy on the seat inventory control in railway passenger transport. The author proposes one optimization model for seats allocation, which is to allocate seats by calculating the value to determine the order of getting seats for each OD under the analysis of the interaction among all trains running on the same rail lines and those trains’ demands intensity. The experiment results show that this new model can be used to allocate seats for the train with multiple stations rapidly and appropriately. In comparison with the method without considering the value of each OD, the model based on OD’s value can get better results.

scholarly journals Halt Optimization Strategy for a Space Manipulator with a Joint-Locked Failure

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Gang Chen ◽  
Lanpu Li ◽  
Yingzhuo Fu ◽  
Bonan Yuan ◽  
Junting Fei
Keyword(s):  
Optimization Model ◽  
Optimal Configuration ◽  
Optimization Strategy ◽  
Configuration Optimization ◽  
Space Manipulator ◽  
Motion Optimization ◽  
Disturbance Torque

Aiming at a space manipulator with a joint-locked failure, a halt optimization strategy is proposed in this paper. Firstly, a halt configuration optimization model (HCOM) is constructed, to select an optimal configuration where the kinematic ability of the manipulator is the best. Secondly, considering the constraint of joint running parameters and the disturbance torque of the base, we construct and solve the halt motion optimization model (HMOM), which can achieve a steady halt and ensure the safety of the manipulator. The correctness and effectiveness of the proposed strategy in this paper are verified with a 7-DOF space manipulator. This strategy firstly puts forward the idea of halt configuration optimization and realizes the minimum global disturbance torque of the base in the halt process.

scholarly journals An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5718
Author(s):  
Kalim Ullah ◽  
Sajjad Ali ◽  
Taimoor Ahmad Khan ◽  
Imran Khan ◽  
Sadaqat Jan ◽  
...  
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Carbon Emission ◽  
Renewable Energy Sources ◽  
Operating Cost ◽  
Energy Sources ◽  
Optimization Strategy ◽  
Model Based ◽  
Demand Response Programs ◽  
Operation Cost

An energy optimization strategy is proposed to minimize operation cost and carbon emission with and without demand response programs (DRPs) in the smart grid (SG) integrated with renewable energy sources (RESs). To achieve optimized results, probability density function (PDF) is proposed to predict the behavior of wind and solar energy sources. To overcome uncertainty in power produced by wind and solar RESs, DRPs are proposed with the involvement of residential, commercial, and industrial consumers. In this model, to execute DRPs, we introduced incentive-based payment as price offered packages. Simulations are divided into three steps for optimization of operation cost and carbon emission: (i) solving optimization problem using multi-objective genetic algorithm (MOGA), (ii) optimization of operating cost and carbon emission without DRPs, and (iii) optimization of operating cost and carbon emission with DRPs. To endorse the applicability of the proposed optimization model based on MOGA, a smart sample grid is employed serving residential, commercial, and industrial consumers. In addition, the proposed optimization model based on MOGA is compared to the existing model based on multi-objective particle swarm optimization (MOPSO) algorithm in terms of operation cost and carbon emission. The proposed optimization model based on MOGA outperforms the existing model based on the MOPSO algorithm in terms of operation cost and carbon emission. Experimental results show that the operation cost and carbon emission are reduced by 24% and 28% through MOGA with and without the participation of DRPs, respectively.

scholarly journals Optimization and Experiment of Mass Compensation Strategy for Built-In Mechanical On-Line Dynamic Balancing System

2020 ◽  
Vol 10 (4) ◽  
pp. 1464
Author(s):  
Zhan Wang ◽  
Bo Zhang ◽  
Ke Zhang ◽  
Guodong Yue
Keyword(s):  
Residual Stress ◽  
Optimization Model ◽  
Dynamic Balance ◽  
Maximum Amplitude ◽  
Optimization Strategy ◽  
Compensation Strategy ◽  
Dynamic Balancing ◽  
Balance System ◽  
Before And After ◽  
On Line

In order to solve the problem of low precision and efficiency in the balancing process due to the movement of balance counterweights in a built-in mechanical on-line dynamic balance system, an optimization strategy for the mass compensation of the mechanical on-line dynamic balancing system is proposed, and a mass compensation optimization model is established. The optimization model takes the phase of counterweight movement as the optimization variable and the residual stress under dynamic balance as the optimization objective. Through the optimization model, the movement phase of the counterweight can be calculated, and the counterweight can be moved to a balanced position that significantly reduces the degree of unbalance. An experiment platform was built to carry out comparison experiments under different rotating speeds and unbalance levels. By comparing the residual stress, amplitude, and dynamic balancing time of the spindle before and after the balance, the accuracy of the phase of the counterweight that is calculated by the optimization model is verified. The optimized dynamic balance compensation strategy and the unoptimized were compared by experiments at different rotating speeds. The experimental results showed that, compared to the unoptimized balance, the amplitude of the spindle after optimizing balance with a dynamic balancing device can decrease by 30.39% on average, with its maximum amplitude decreasing by up 50.18%, and the balancing time can decrease by 31.72% on average, with its maximum balancing time decreasing by up to 43.86%. The research results showed that an optimization strategy can effectively improve dynamic balance efficiency and greatly reduce vibration amplitude, which provides the necessary theoretical basis for improving the running precision of the spindle system.

scholarly journals An Optimal Allocation Strategy for Multienergy Networks Based on Double-Layer Nondominated Sorting Genetic Algorithms

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Min Mou ◽  
Da Lin ◽  
Yuhao Zhou ◽  
Wenguang Zheng ◽  
Jiongming Ruan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Double Layer ◽  
Optimization Model ◽  
Optimal Allocation ◽  
Environmental Benefits ◽  
Optimization Strategy ◽  
Allocation Strategy ◽  
Photovoltaic Panel ◽  
Wind Power Generator ◽  
Nondominated Sorting

Aiming at the problems of complex structures, variable loads, and fluctuation of power outputs of multienergy networks, this paper proposes an optimal allocation strategy of multienergy networks based on the double-layer nondominated sorting genetic algorithm, which can optimize the allocation of distributed generation (DG) and then improve the system economy. In this strategy, the multiobjective nondominated sorting genetic algorithm is adopted in both layers, and the second-layer optimization is based on the optimization result of the first layer. The first layer is based on the structure and load of the multienergy network. With the purpose of minimizing the active power loss and the node voltage offset, an optimization model of the multienergy network is established, which uses the multiobjective nondominated sorting genetic algorithm to solve the installation location and the capacity of DGs in multienergy networks. In the second layer, according to the optimization results of the first layer and the characteristics of different DGs (wind power generator, photovoltaic panel, microturbine, and storage battery), the optimization model of the multienergy network is established to improve the economy, reliability, and environmental benefits of multienergy networks. It uses the multiobjective nondominated sorting genetic algorithm to solve the allocation capacity of different DGs so as to solve the optimal allocation problem of node capacity in multienergy networks. The double-layer optimization strategy proposed in this paper greatly promotes the development of multienergy networks and provides effective guidance for the optimal allocation and reliable operation of multienergy networks.

scholarly journals Energy-Efficient Distributed Packet Scheduling Optimization Strategy in Cooperative Vehicle Infrastructure Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liang Dai ◽  
Tianquan Chen ◽  
Yiming Zhai ◽  
Guiping Wang
Keyword(s):  
Energy Consumption ◽  
Optimization Model ◽  
Queue Length ◽  
Packet Scheduling ◽  
Optimization Theory ◽  
Optimization Strategy ◽  
Scheduling Optimization ◽  
Delay Guarantee ◽  
Self Powered ◽  
Knapsack Algorithm

In the cooperative vehicle infrastructure system (CVIS), due to the limitation of deployment conditions, some roadside units (RSUs) need to use renewable energy to supply power and transmit the fused sensor network’s data to the backbone network through the passing vehicles. Aiming at the problem of energy consumption and time delay guarantee of multiple self-powered RSUs in the CVIS, a distributed packet scheduling optimization strategy for energy-delay trade-off in self-powered RSUs is proposed. The strategy can minimize the system energy consumption by constraining the packet queue length of the self-powered RSUs. A dynamic optimization model of distributed packet adaptive scheduling for multiple self-powered RSUs is established based on the Lyapunov optimization theory. Based on the knapsack algorithm, the analytical algorithm of the optimization model is proposed. The simulation results show that the packet scheduling strategy can reduce the energy consumption and delay of the system by satisfying the upper limit of the packet queue length.

scholarly journals Optimization Strategy of Configuration and Scheduling for User-Side Energy Storage

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 120
Author(s):  
Yushan Liu ◽  
Qianqian Liu ◽  
Huaimin Guan ◽  
Xiao Li ◽  
Daqiang Bi ◽  
...  
Keyword(s):  
Energy Storage ◽  
Optimization Model ◽  
Lithium Iron Phosphate ◽  
Economic Benefits ◽  
Forecast Errors ◽  
Loop Scheduling ◽  
Optimization Strategy ◽  
Scheduling Optimization ◽  
Power Fluctuations ◽  
The Impact

In order to reduce the impact of load power fluctuations on the power system and ensure the economic benefits of user-side energy storage operation, an optimization strategy of configuration and scheduling based on model predictive control for user-side energy storage is proposed in this study. Firstly, considering the cost and benefits of energy storage comprehensively, an energy storage configuration optimization model with the highest annualized net income as the goal is built to determine the parameters for configuring energy storage. Then, with the goal of maximizing the profit during the scheduling period, pre-month scheduling optimization model, day-ahead scheduling optimization model and intra-day scheduling optimization model are established. The goal of the pre-month scheduling optimization model is to determine the maximum monthly demand; part of the scheduling results in the day-ahead scheduling optimization model directly participate in the intra-day scheduling; the intra-day rolling optimization relies on the advantages of real-time feedback and closed-loop scheduling to smooth out power fluctuations caused by load forecast errors. Finally, the configuration and economic benefit of lithium iron phosphate batteries, lead-carbon batteries and sodium-sulfur batteries are analyzed and compared, and scheduling analysis is performed. The simulation results show that the proposed optimization method can cut peaks and fill valleys, ensure the economic benefits of users, and provide guidance for users to invest in energy storage.

scholarly journals Reliability Optimization Model of Stop-Skipping Bus Operation with Capacity Constraints

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhenhua Mou ◽  
Hu Zhang ◽  
Shidong Liang
Keyword(s):  
Optimization Model ◽  
Simulation Method ◽  
Service Level ◽  
Capacity Constraints ◽  
Reliability Optimization ◽  
Optimization Strategy ◽  
Operation System ◽  
Passenger Demand ◽  
Changchun City ◽  
The Stability

In a bus line with high passenger demand, the stop-skipping operation can benefit both users and operators as well as improve the service level of bus line, but it usually cannot be effectively adopted by operators because of the unordered operation of buses and the discomfort of waiting passenger at stops. Therefore, the stop-skipping operation requires higher reliability for stop serving. This paper proposes a reliable stop-skipping service design with holding strategy by taking into account bus capacity constraints. Under a stop-skipping service, the holding strategy is used to balance the interval of stop serving rather than the headway of buses. Meanwhile, to reflect the actual boarding process of waiting passengers, the number of left-over passengers and the waiting time are calculated during serving time and holding time, respectively. The objective function is to minimize the total costs of bus operation system. Besides, a Genetic Algorithm combined with Monte Carlo Simulation method is defined and implemented to solve the reliability optimization model. Finally, a numerical example based on a bus route in Changchun city is carried out to test the reliability optimization model. Results showed that the reliability optimization strategy can improve the stability of stop service and then save cost of passengers’ travel time.

An optimization model of self-paced tracking

1984 ◽  
Author(s):  
M. A. Montazer ◽  
Colin G. Drury
Keyword(s):  
Optimization Model
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