• 한국농림기상학회지
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• 제21권2호
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• pp.75-84
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• 2019

이 연구의 목적은 random forest 를 활용하여 기상요소만을 이용하여 우리나라 전체의 벼 평균수량을 예측하는데 있다. Random forest 는 예측에 사용되는 각 predictor variable 을 분리할 수 있는데 이를 통해 분리된 시계열 상의 추세가 비정상적인 증가형태를 보였다. 이는 결국 예측능력의 저하로 이어지기 때문에 이를 제거할 필요가 있고 본 연구에서는 이동 평균을 이용하여 제거한 후 예측을 하였다. 1991 년부터 2005 년까지의 기상자료와 수량자료를 학습에 사용하였고 2006 년부터 2015 년까지의 자료들을 검증용으로 사용하였다. 학습자료에 대해서는 상당히 정확한 예측 능력을 보여주었으나 검증 자료에서는 그렇지 못하였다. 그 이유를 분석하기 위해 학습 자료와 검증자료에 대해서 각각 변수 중요도를 산출하여 비교한 결과 두 자료 간에 월별 기상 자료에 대한 중요도가 변동되었음을 발견하였다. 이러하 차이가 발생한 이유는 학습자료와 검증 자료에서의 전국적으로 표준이앙기가 이동하여 벼의 생육기간 자체가 변하였기 때문이다. 따라서, 정확한 예측을 위해서는 지역별 파종기 또는 이앙기에 대한 자료가 필요하며 단순히 기상 자료만을 활용한 예측은 어려운 것으로 생긱된다.

• 대한산업공학회지
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• 제40권5호
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• pp.477-480
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• 2014

The single-period production inventory control problem under random yield is considered to analyze the impact of the yield characteristics on the firm's profit. We use the stochastic comparison as a main vehicle to compare the profits resulted under different random yields. Commonly used stochastic orderings are addressed with an analysis of their implications on the firm's profit. Moreover, a distribution-free bound on the profit is derived.

• Management Science and Financial Engineering
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• 제22권1호
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• pp.1-4
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• 2016

We consider a two-tier supply chain with multiplicative random yield. We focus on the supply chain performance with respect to the control scheme of determining the production lot size. The profit loss due to distributed control is analyzed to give an insight for devising efficient supply contracts.

• Asian-Australasian Journal of Animal Sciences
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• 제19권7호
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• pp.915-921
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• 2006

Various random regression models with different order of Legendre polynomials for permanent environmental and genetic effects were constructed to predict future milk yield of Holstein cows in Korea. A total of 257,908 test-day (TD) milk yield records from a total of 28,135 cows belonging to 1,090 herds were considered for estimating (co)variance of the random covariate coefficients using an expectation-maximization REML algorithm in an animal mixed model. The variances did not change much between the models, having different order of Legendre polynomial, but a decreasing trend was observed with increase in the order of Legendre polynomial in the model. The R-squared value of the model increased and the residual variance reduced with the increase in order of Legendre polynomial in the model. Therefore, a model with $5^{th}$ order of Legendre polynomial was considered for predicting future milk yield. For predicting the future milk yield of cows, 132,771 TD records from 28,135 cows were randomly selected from the above data by way of preceding partial TD record, and then future milk yields were estimated using incomplete records from each cow randomly retained. Results suggested that we could predict the next four months milk yield with an error deviation of 4 kg. The correlation of more than 70% between predicted and observed values was estimated for the next four months milk yield. Even using only 3 TD records of some cows, the average milk yield of Korean Holstein cows would be predicted with high accuracy if compared with observed milk yield. Persistency of each cow was estimated which might be useful for selecting the cows with higher persistency. The results of the present study suggested the use of a $5^{th}$ order Legendre polynomial to predict the future milk yield of each cow.

• 한국경영과학회지
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• 제17권2호
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• pp.107-115
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• 1992

Many manufacturing processes involved in the fabrication and assembly of hightech components have highly variable yields that tend to complicate the production control. Under this random yield situation we develop a model to determine optimal input quantity, mean waiting time in the system and variance of waiting time in the system. An example which considers beta distribution as a yield distribution is given.

• 품질경영학회지
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• 제40권4호
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• pp.441-452
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• 2012

Purpose: In this paper, we have considered a problem of newsvendor model in an environment of random yields in quality and customer balking behavior, in which only the mean and the variance of the demand are known. In practice, the distributional information of the demand is very limited and only the mean and variance are guessed by experience. In addition, due to the customers balking behavior occurring when the available inventory level decreases, the product's demand becomes a function of inventory level so that the classical newsvendor's optimal order quantity is no longer optimal. Methods: We have developed an optimal order quantity model that enables us to incorporate the random yield of a product and the customer balking information such as a threshold inventory level of balking and the corresponding probability of a sale during the balking. Results: We illustrated the concepts developed here through simple numerical examples and showed the robustness of our model in a various setting of parameters. Conclusion: This paper provides a useful analysis showing that our distribution-specific and distribution-free approach to the optimal order quantity in the newsboy model can act as an effective tools to match supply with demand for these product lines.

• Asian-Australasian Journal of Animal Sciences
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• 제30권10호
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• pp.1382-1387
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• 2017

Objective: During the last decade, genetic evaluation of dairy cows using longitudinal data (test day milk yield or 305-day milk yield) using random regression method has been officially adopted in several countries. The objectives of this study were to estimate covariance functions for genetic and permanent environmental effects and to obtain genetic parameters of 305-day milk yield over seven parities. Methods: Data including 60,279 total 305-day milk yield of 17,309 Iranian Holstein dairy cows in 7 parities calved between 20 to 140 months between 2004 and 2011. Residual variances were modeled by homogeneous and step functions with 7 and 10 classes. Results: The results showed that a third order polynomial for additive genetic and permanent environmental effects plus a step function with 10 classes for the residual variance was the most adequate and parsimonious model to describe the covariance structure of the data. Heritability estimates obtained by this model varied from 0.17 to 0.28. The performance of this model was better than repeatability model. Moreover, 10 classes of residual variance produce the more accurate result than 7 classes or homogeneous residual effect. Conclusion: A quadratic Legendre polynomial for additive genetic and permanent environmental effects with 10 step function residual classes are sufficient to produce a parsimonious model that explained the change in 305-day milk yield over consecutive parities of Iranian Holstein cows.

• 대한산업공학회지
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• 제41권3호
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• pp.324-329
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• 2015

Though the supplier diversification is considered as a vital tool to mitigate the risk due to supply chain disruptions, there are results which show the optimality of the sole sourcing. This paper further generalizes the results to show that the sole sourcing is optimal under very mild conditions. Discussion on why the sole sourcing is optimal is given with the insight on the value of supplier diversification.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.17-28
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• 2023

Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.

• Asian-Australasian Journal of Animal Sciences
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• 제29권6호
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• pp.775-781
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• 2016

A single trait linear mixed random regression test-day model was applied for the first time for analyzing the first lactation monthly test-day milk yield records in Karan Fries cattle. The test-day milk yield data was modeled using a random regression model (RRM) considering different order of Legendre polynomial for the additive genetic effect (4th order) and the permanent environmental effect (5th order). Data pertaining to 1,583 lactation records spread over a period of 30 years were recorded and analyzed in the study. The variance component, heritability and genetic correlations among test-day milk yields were estimated using RRM. RRM heritability estimates of test-day milk yield varied from 0.11 to 0.22 in different test-day records. The estimates of genetic correlations between different test-day milk yields ranged 0.01 (test-day 1 [TD-1] and TD-11) to 0.99 (TD-4 and TD-5). The magnitudes of genetic correlations between test-day milk yields decreased as the interval between test-days increased and adjacent test-day had higher correlations. Additive genetic and permanent environment variances were higher for test-day milk yields at both ends of lactation. The residual variance was observed to be lower than the permanent environment variance for all the test-day milk yields.