An optimal combination prediction method of turnover spare parts consumption based on certain weight

Nowadays we mainly take a method with proportion-coefficient basing experience to predict the carrying maintenance spare parts of armored equipments in wartime. However, the method does not accord with the expending law of maintenance spare parts in wartime. So in the paper we take support degree model and a method with phase support degree to define the rough carrying amount firstly for settling the problem. Then we take marginal utility analysis basing the restriction of transporting capability to optimizing the carrying amount of spare parts in wartime.

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Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era

PLoS ONE ◽

10.1371/journal.pone.0259284 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0259284

Author(s):

Hailan Ran

Keyword(s):

Genetic Algorithm ◽

Supply Chain ◽

Inventory Management ◽

Management System ◽

Prediction Method ◽

Spare Parts ◽

Collaborative Computing ◽

Demand Prediction ◽

Industrial Enterprises ◽

Inventory Management System

The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.

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A Spare Parts Demand Prediction Method for Wind Farm Based on Periodic Maintenance Strategy

Volume 2: I&C, Digital Controls, and Influence of Human Factors; Plant Construction Issues and Supply Chain Management; Plant Operations, Maintenance, Aging Management, Reliability and Performance; Renewable Energy Systems: Solar, Wind, Hydro and Geothermal; Risk Management, Safety and Cyber Security; Steam Turbine-Generators, Electric Generators, Transformers, Switchgear, and Electric BOP and Auxiliaries; Student Competition; Thermal Hydraulics and Computational Fluid Dynamics ◽

10.1115/power-icope2017-3077 ◽

2017 ◽

Cited By ~ 1

Author(s):

Chen Zhang ◽

Tao Yang ◽

Wei Gao ◽

Weiqiu Chen ◽

Jing He ◽

...

Keyword(s):

Wind Farm ◽

Discrete Event ◽

Demand Forecasting ◽

Prediction Method ◽

Wind Farms ◽

Assessment Method ◽

Spare Parts ◽

Maintenance Strategy ◽

Demand Prediction ◽

Periodic Maintenance

Nowadays, the management level and information construction of wind power industry are still relatively backward, for example, the existing maintenance models for wind farm are much too single, and corrective maintenance strategy is the most commonly used, which means that maintenance measures are initiated only after a breakdown occurs in the system. Moreover, the wind farm spare parts management is out-dated, no practical and accurate spares demand assessment method is available. In order to enrich the choices of maintenance methods and eliminate the subjective influence in the demand analysis of spare parts, a spare parts demand prediction method for wind farm based on periodic maintenance strategy considering combination of different maintenance models for wind farms is proposed in this paper, which consists of five major steps, acquire the reliability functions of components, establish the maintenance strategy, set the maintenance parameters, maintenance strategy simulation and spare parts demand prediction. The discrete event simulation method is used to solve the prediction model, and results demonstrate the operability and practicality of the proposed demand forecasting method, which can provide guidance for the actual operation and maintenance of wind farms.

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Path integration and visual landmarks: Optimal combination or multiple systems?

PsycEXTRA Dataset ◽

10.1037/e520592012-897 ◽

2010 ◽

Cited By ~ 2

Author(s):

Mintao Zhao ◽

William H. Warren

Keyword(s):

Path Integration ◽

Optimal Combination ◽

Visual Landmarks ◽

Multiple Systems

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Maksimalisasi Keuntungan Dengan Pendekatan Metode Simpleks Kasus Pada Pabrik Sosis SM

Liquidity ◽

10.32546/lq.v2i1.130 ◽

2018 ◽

Vol 2 (1) ◽

pp. 59-65 ◽

Cited By ~ 2

Author(s):

Yanti Budiasih

Keyword(s):

Linear Programming ◽

Simplex Method ◽

Optimal Combination ◽

Optimal Input ◽

Maximum Profit ◽

Input Combination

The purpose of this study are to (1) determine the combination of inputs used in producing products such as beef sausages and veal sausage meatball; and (2) determine the optimal combination whether the product can provide the maximum profit. In order to determine the combination of inputs and maximum benefits can be used linear programming with graphical and simplex method. The valuation result shows that the optimal input combination would give a profit of Rp. 1.115 million per day.

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Selection of a leading edge noise prediction method for PNoise

The Academic Society Journal ◽

10.32640/tasj.2018.4.214 ◽

2018 ◽

pp. 214-223

Author(s):

AM Faria ◽

MM Pimenta ◽

JY Saab Jr. ◽

S Rodriguez

Keyword(s):

Wind Turbine ◽

Turbine Blades ◽

Prediction Method ◽

Leading Edge ◽

Wind Farms ◽

Broadband Noise ◽

Turbulence Energy ◽

Noise Prediction ◽

Migration Routes ◽

Turbulent Inflow

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.

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