scholarly journals A Procurement Method in Oil Marketing Company Based on Forecast Model and Expectation Criterion

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhihong Zhao ◽  
Yunjin Wang
Keyword(s):  
Forecast Model ◽  
Good Effect ◽  
Purchase Decision ◽  
Group Method ◽  
Data Handling ◽  
Fusion Model ◽  
Optimal Method ◽  
Safety Stock ◽  
Procurement Method ◽  
Marketing Operation

Within the oil marketing operation, various entities compete and attempt to maximize their profits by providing sufficient supply to meet needs of market. It is an optimal method for oil marketing company operation with a dynamically reasonable inventory to maximize profit under oil price fluctuation and inventory sales lag. In this paper, we study the optimal procurement method of oil marketing company which confirms the reasonable inventory. We build a data fusion model for the GMDH- (group method of data handling-) type neural network and normal distribution forecast results, what is trying to confirm the safety stock (SS). On the basis of the expectation criterion of risk decision and safety stock limit, oil marketing companies can make scientific purchase decision for inventory income. Numerical results reveal that this method has a good effect for inventory income.

APPLICATION OF ONE WEEK AHEAD WAVE PREDICTION FOR USING GROUP METHOD OF DATA HANDLING

2020 ◽  
Vol 76 (2) ◽  
pp. I_229-I_234
Author(s):  
Keishiro CHIYONOBU ◽  
Sooyoul KIM ◽  
Masahide TAKEDA ◽  
Chisato HARA ◽  
Hajime MASE ◽  
...  
Keyword(s):  
Group Method ◽  
Data Handling ◽  
Wave Prediction

Scoring of Tenders in Construction Projects Using Group Method of Data Handling

2020 ◽  
Vol 24 (7) ◽  
pp. 1996-2008
Author(s):  
Masoud Nouri Mehrabani ◽  
Emadaldin Mohammadi Golafshani ◽  
Mehdi Ravanshadnia
Keyword(s):  
Construction Projects ◽  
Group Method ◽  
Data Handling

Methods of Mathematical Modeling of the Leaching Process of Cobalt-Containing Solutions

2021 ◽  
Vol 316 ◽  
pp. 661-666
Author(s):  
Nataliya V. Mokrova
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Model ◽  
Chemical Kinetics ◽  
Group Method ◽  
Data Handling ◽  
Multistage Processes ◽  
Leaching Process ◽  
Proposed Model ◽  
Simulation Results ◽  
The Mathematical Model

Current cobalt processing practices are described. This article discusses the advantages of the group argument accounting method for mathematical modeling of the leaching process of cobalt solutions. Identification of the mathematical model of the cascade of reactors of cobalt-producing is presented. Group method of data handling is allowing: to eliminate the need to calculate quantities of chemical kinetics; to get the opportunity to take into account the results of mixed experiments; to exclude the influence of random interference on the simulation results. The proposed model confirms the capabilities of the group method of data handling for describing multistage processes.

Predicting carcass energy content and composition in broilers using the group method of data handling-type neural networks

2010 ◽  
Vol 149 (2) ◽  
pp. 249-254 ◽  
Author(s):  
A. FARIDI ◽  
M. MOTTAGHITALAB ◽  
H. DARMANI-KUHI ◽  
J. FRANCE ◽  
H. AHMADI
Keyword(s):  
Neural Networks ◽  
Body Weight ◽  
Goodness Of Fit ◽  
Carcass Composition ◽  
Poultry Meat ◽  
Alternative Methods ◽  
Group Method ◽  
Meat Production ◽  
Data Handling ◽  
Input Variables

SUMMARYThe success of poultry meat production has been strongly related to improvements in growth and carcass yield, mainly by increasing breast proportion and reducing carcass fat. Conventional laboratory techniques for determining carcass composition are expensive, cumbersome and time consuming. These disadvantages have prompted a search for alternative methods. In this respect, the potential benefits from modelling growth are considerable. Neural networks (NNs) are a relatively new option for modelling growth in animal production systems. One self-organizing sub-model of artificial NN is the group method of data handling-type NN (GMDH-type NN). The present study aimed at applying the GMDH-type NNs to data from two studies with broilers in order to predict carcass energy (CEn, MJ/g) content and relative growth (g/g of body weight) of carcass components (carcass protein, breast muscle, leg and thigh muscles, carcass fat, abdominal fat, skin fat and visceral fat). The effective input variables involved in the prediction of CEn and carcass fat content using data from the first study were dietary metabolizable energy (ME, kJ/kg), crude protein (CP, g/kg of diet), fat (g/kg of diet) and crude fibre (CF, g/kg of diet). For data from the second study, the effective input variables involved in the prediction of carcass components were dietary ME (MJ/kg), CP (g/kg of diet), methionine (g/kg of diet), lysine (g/kg of diet) and body weight (kg). Quantitative examination of the goodness of fit, using R2 and error measurement indices, for the predictive models proposed by the GMDH-type NN revealed close agreement between observed and predicted values of CEn and carcass components.

The Research of Railway Passenger Flow Prediction Model Based on BP Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2366-2369 ◽  
Author(s):  
Yao Wang ◽  
Dan Zheng ◽  
Shi Min Luo ◽  
Dong Ming Zhan ◽  
Peng Nie
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Good Effect ◽  
Passenger Flow ◽  
Model Based ◽  
Flow Prediction ◽  
Transport Organization ◽  
Railway Passenger ◽  
Short Time

Based on analyzing the principle of BP neural network and time sequence characteristics of railway passenger flow, the forecast model of railway short-term passenger flow based on BP neural network was established. This paper mainly researches on fluctuation characteristics and short-time forecast of holiday passenger flow. Through analysis of passenger flow and then be used in passenger flow forecasting in order to guide the transport organization program especially the train plan of extra passenger train. And the result shows the forecast model based on BP neural network has a good effect on railway passenger flow prediction.

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