中小企業(yè)肉類產(chǎn)品短期需求預測模型研究
發(fā)布時間：2019-03-29 19:49
【摘要】：近年來,隨著中國在國際上影響力的提升,我國食品企業(yè)所面臨的競爭壓力逐漸增大。為了更好地參與國際競爭以及應對國內(nèi)產(chǎn)業(yè)結(jié)構(gòu)變化,中小型企業(yè)必須要推動自身管理體制的改革。目前我國食品企業(yè)普遍面臨庫存占用過大的問題,已成為制約企業(yè)發(fā)展的瓶頸,易變質(zhì)及存儲成本高的肉類產(chǎn)品特性促使此庫存問題尤為突出。引發(fā)此類問題的主要原因是對短期需求預測不精確,因此有必要對中小企業(yè)肉類產(chǎn)品的短期需求預測進行研究。鑒于此,本文基于灰色理論、BP (Back Propagation,反向傳播)神經(jīng)網(wǎng)絡、SVM (Support Vector Machine,支持向量機)及 GA( Genetic Algorithm,遺傳算法)構(gòu)建肉類產(chǎn)品短期需求預測模型。通過實驗對比,選取出精確度較高且擬合度較好的預測模型。首先,選取中小企業(yè)肉類產(chǎn)品短期需求的主要影響因素。根據(jù)影響因素選取原則,最終選取月度CPI (Consumer Price Index,居民消費價格指數(shù))、產(chǎn)品價格、促銷支出成本、季節(jié)系數(shù)和節(jié)假日系數(shù)5個主要因素。其次,選擇預測方法。分析現(xiàn)有的預測方法,對比傳統(tǒng)預測方法和人工智能預測方法,選取預測精確度較高的人工智能預測方法進行研究。并從人工智能預測方法中選取較為常用的BP神經(jīng)網(wǎng)絡和SVM兩種預測方法。然后,構(gòu)建預測模型。本文提出將灰色預測理論同BP神經(jīng)網(wǎng)絡和SVM相結(jié)合,避免因樣本數(shù)據(jù)隨機性過大導致預測精度降低的問題。為了解決BP神經(jīng)網(wǎng)絡和SVM的初始參數(shù)值具有隨機性的問題,引入GA分別對兩個預測方法進行優(yōu)化。最后,對比分析實驗結(jié)果。利用灰色BP神經(jīng)網(wǎng)絡、灰色GA-BP神經(jīng)網(wǎng)絡、灰色SVM及灰色GA-SVM預測模型分別對肉類產(chǎn)品月需求量進行預測,并依據(jù)后驗差檢驗法和殘差檢驗法對四個模型進行評價。研究表明,灰色GA-SVM模型對中小型企業(yè)肉類產(chǎn)品的月需求量預測精確度較高且擬合度較好。
[Abstract]:In recent years, with the promotion of China's influence in the world, the competition pressure of Chinese food enterprises is increasing gradually. In order to better participate in international competition and cope with the change of domestic industrial structure, small and medium-sized enterprises must promote the reform of their own management system. At present, food enterprises in China are generally faced with the problem of excessive inventory occupation, which has become a bottleneck restricting the development of enterprises. The characteristics of meat products, which are prone to deterioration and high storage costs, make this inventory problem particularly prominent. The main cause of this kind of problem is the imprecise short-term demand forecast, so it is necessary to study the short-term demand forecast of small and medium-sized meat products. In view of this, this paper constructs the short-term demand forecasting model of meat products based on grey theory, BP (Back Propagation, back propagation (, SVM (Support Vector Machine, support vector machine) and GA (Genetic Algorithm, genetic algorithm (GA). Through the comparison of experiments, the prediction model with high accuracy and good fitting degree is selected. First of all, select the main influencing factors of short-term demand for meat products of small and medium-sized enterprises. According to the selection principle of influencing factors, five main factors are selected: monthly CPI (Consumer Price Index, consumer price index (CPI), product price, promotion cost, seasonal coefficient and holiday coefficient. Secondly, the prediction method is selected. The existing forecasting methods are analyzed, compared with the traditional forecasting methods and artificial intelligence forecasting methods, and the artificial intelligence forecasting method with high prediction accuracy is selected to carry on the research. The commonly used BP neural network and SVM prediction method are selected from the artificial intelligence prediction method. Then, the prediction model is constructed. In this paper, the grey prediction theory is combined with BP neural network and SVM to avoid the problem of decreasing the prediction precision due to the large randomness of sample data. In order to solve the problem that the initial parameter values of BP neural network and SVM are random, GA is introduced to optimize the two prediction methods respectively. Finally, the experimental results are compared and analyzed. Grey BP neural network, grey GA-BP neural network, grey SVM and grey GA-SVM forecasting models were used to forecast the monthly demand of meat products, and the four models were evaluated according to the posterior error test and the residual test. The results show that the grey GA-SVM model can predict the demand of meat products in small and medium-sized enterprises with high accuracy and good fitting degree.
【學位授予單位】：山東科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F276.3;TP18
本文編號：2449829
[Abstract]:In recent years, with the promotion of China's influence in the world, the competition pressure of Chinese food enterprises is increasing gradually. In order to better participate in international competition and cope with the change of domestic industrial structure, small and medium-sized enterprises must promote the reform of their own management system. At present, food enterprises in China are generally faced with the problem of excessive inventory occupation, which has become a bottleneck restricting the development of enterprises. The characteristics of meat products, which are prone to deterioration and high storage costs, make this inventory problem particularly prominent. The main cause of this kind of problem is the imprecise short-term demand forecast, so it is necessary to study the short-term demand forecast of small and medium-sized meat products. In view of this, this paper constructs the short-term demand forecasting model of meat products based on grey theory, BP (Back Propagation, back propagation (, SVM (Support Vector Machine, support vector machine) and GA (Genetic Algorithm, genetic algorithm (GA). Through the comparison of experiments, the prediction model with high accuracy and good fitting degree is selected. First of all, select the main influencing factors of short-term demand for meat products of small and medium-sized enterprises. According to the selection principle of influencing factors, five main factors are selected: monthly CPI (Consumer Price Index, consumer price index (CPI), product price, promotion cost, seasonal coefficient and holiday coefficient. Secondly, the prediction method is selected. The existing forecasting methods are analyzed, compared with the traditional forecasting methods and artificial intelligence forecasting methods, and the artificial intelligence forecasting method with high prediction accuracy is selected to carry on the research. The commonly used BP neural network and SVM prediction method are selected from the artificial intelligence prediction method. Then, the prediction model is constructed. In this paper, the grey prediction theory is combined with BP neural network and SVM to avoid the problem of decreasing the prediction precision due to the large randomness of sample data. In order to solve the problem that the initial parameter values of BP neural network and SVM are random, GA is introduced to optimize the two prediction methods respectively. Finally, the experimental results are compared and analyzed. Grey BP neural network, grey GA-BP neural network, grey SVM and grey GA-SVM forecasting models were used to forecast the monthly demand of meat products, and the four models were evaluated according to the posterior error test and the residual test. The results show that the grey GA-SVM model can predict the demand of meat products in small and medium-sized enterprises with high accuracy and good fitting degree.
【學位授予單位】：山東科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F276.3;TP18
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