基于用電量的制造企業(yè)剩余生產(chǎn)能力預(yù)測
發(fā)布時間：2018-10-30 18:59
【摘要】：在供應(yīng)鏈中，采購商往往有著眾多的制造供應(yīng)商可供選擇，在采購決策之前，，需要對供應(yīng)商進行評估，掌握供應(yīng)商訂單期內(nèi)剩余生產(chǎn)能力信息是關(guān)鍵評估內(nèi)容之一。由于采購商與制造供應(yīng)商之間不存在所屬關(guān)系，評估涉及到的訂單、生產(chǎn)計劃等信息屬于企業(yè)隱私，同時供應(yīng)商為了追逐利益最大化，極有可能提供虛假信息，因此如何獲取準確客觀的生產(chǎn)信息成為掌握供應(yīng)商剩余生產(chǎn)能力信息的瓶頸。針對這一問題，根據(jù)用電量信息難以造假、容易考證、獲取方便、能夠反映現(xiàn)實生產(chǎn)情況等特征，提出了基于用電量預(yù)測制造供應(yīng)商剩余生產(chǎn)能力的方法，同時也為制造企業(yè)掌握自身未來生產(chǎn)情況提供新的思路。 論文從制造系統(tǒng)角度分析了基于用電量進行制造企業(yè)剩余生產(chǎn)能力預(yù)測的可行性，然后進行建模。建模分三個過程進行：制造企業(yè)月用電量與該月剩余生產(chǎn)能力關(guān)系分析建模、基于企業(yè)月用電量時間序列對未來月份剩余生產(chǎn)能力的預(yù)測建模、模型驗證。 首先，根據(jù)企業(yè)用電分布，分析了用電量除了受產(chǎn)品生產(chǎn)耗電影響之外，時節(jié)氣候變化是主要的非生產(chǎn)影響因素，其具有增長性和季節(jié)波動特點，引入用電量時節(jié)影響變量概念描述時節(jié)氣候影響因素，利用部分樣本求出用電量與產(chǎn)量、剩余生產(chǎn)能力的線性回歸關(guān)系，再對各月用電量時節(jié)影響變量進行量化計算，從而得到了整體樣本用電量與產(chǎn)量、剩余生產(chǎn)能力的非線性回歸關(guān)系。 其次，根據(jù)灰色系統(tǒng)理論“少數(shù)據(jù)、貧信息、增長性”的特點，結(jié)合生產(chǎn)能力度量、預(yù)測期內(nèi)用電量時節(jié)影響變量計算、剩余生產(chǎn)能力利用率計算，進行了基于用電量的制造企業(yè)剩余生產(chǎn)能力灰色預(yù)測，分析誤差產(chǎn)生原因，利用突變數(shù)據(jù)去除法、趨勢移動平均法改進灰色預(yù)測技術(shù)，建立了最終預(yù)測模型，并建立了基于用電量進行同類型企業(yè)的剩余生產(chǎn)能力預(yù)測模型。 最后，文章對所建預(yù)測模型進行實例分析，驗證了預(yù)測模型的有效性。
[Abstract]:In the supply chain, buyers often have a large number of manufacturing suppliers to choose from. Before purchasing decision, the supplier needs to be evaluated. It is one of the key evaluation contents to grasp the information of the supplier's surplus production capacity during the order period. Since there is no ownership relationship between the purchaser and the manufacturing supplier, the order, production plan and other information involved in the evaluation is private to the enterprise. In order to maximize the benefits, the supplier is likely to provide false information. Therefore, how to obtain accurate and objective production information becomes the bottleneck to grasp the information of supplier's surplus production capacity. In order to solve this problem, according to the characteristics of electricity consumption information is difficult to fake, easy to verify, easy to obtain, and can reflect the actual production situation, the method of forecasting the surplus production capacity of manufacturing suppliers based on electricity consumption is put forward. At the same time also for manufacturing enterprises to grasp their own future production situation to provide new ideas. This paper analyzes the feasibility of forecasting the surplus production capacity of manufacturing enterprises based on electricity consumption from the point of view of manufacturing system, and then models the model. The modeling is divided into three processes: the analysis and modeling of the relationship between the monthly electricity consumption of manufacturing enterprises and the surplus production capacity in that month, and the prediction and modeling of the remaining production capacity in the future month based on the monthly electricity consumption time series of the manufacturing enterprises, and the model verification. First of all, according to the distribution of electricity consumption in enterprises, it is analyzed that the seasonal climate change is the main non-production factor, which has the characteristics of growth and seasonal fluctuation. The concept of the influence variable of electricity consumption season is introduced to describe the seasonal climate influence factors, and the linear regression relationship between electricity consumption and output, surplus production capacity is obtained by using some samples, and then the quantitative calculation is carried out on the influence variables of each month's electricity consumption season. The nonlinear regression relationship between electricity consumption, output and surplus production capacity of the whole sample is obtained. Secondly, according to the characteristics of grey system theory, such as "less data, poor information, growth", combined with the measurement of production capacity, the calculation of the influence variables and the utilization rate of surplus production capacity in the forecasting period are carried out. In this paper, grey prediction of surplus production capacity of manufacturing enterprises based on electricity consumption is carried out. The causes of errors are analyzed. The grey prediction technology is improved by using abrupt data removal method and trend moving average method, and the final prediction model is established. A model for forecasting the surplus production capacity of the same type of enterprises based on electricity consumption is established. Finally, an example is given to verify the validity of the prediction model.
【學(xué)位授予單位】：寧波大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH186
[Abstract]:In the supply chain, buyers often have a large number of manufacturing suppliers to choose from. Before purchasing decision, the supplier needs to be evaluated. It is one of the key evaluation contents to grasp the information of the supplier's surplus production capacity during the order period. Since there is no ownership relationship between the purchaser and the manufacturing supplier, the order, production plan and other information involved in the evaluation is private to the enterprise. In order to maximize the benefits, the supplier is likely to provide false information. Therefore, how to obtain accurate and objective production information becomes the bottleneck to grasp the information of supplier's surplus production capacity. In order to solve this problem, according to the characteristics of electricity consumption information is difficult to fake, easy to verify, easy to obtain, and can reflect the actual production situation, the method of forecasting the surplus production capacity of manufacturing suppliers based on electricity consumption is put forward. At the same time also for manufacturing enterprises to grasp their own future production situation to provide new ideas. This paper analyzes the feasibility of forecasting the surplus production capacity of manufacturing enterprises based on electricity consumption from the point of view of manufacturing system, and then models the model. The modeling is divided into three processes: the analysis and modeling of the relationship between the monthly electricity consumption of manufacturing enterprises and the surplus production capacity in that month, and the prediction and modeling of the remaining production capacity in the future month based on the monthly electricity consumption time series of the manufacturing enterprises, and the model verification. First of all, according to the distribution of electricity consumption in enterprises, it is analyzed that the seasonal climate change is the main non-production factor, which has the characteristics of growth and seasonal fluctuation. The concept of the influence variable of electricity consumption season is introduced to describe the seasonal climate influence factors, and the linear regression relationship between electricity consumption and output, surplus production capacity is obtained by using some samples, and then the quantitative calculation is carried out on the influence variables of each month's electricity consumption season. The nonlinear regression relationship between electricity consumption, output and surplus production capacity of the whole sample is obtained. Secondly, according to the characteristics of grey system theory, such as "less data, poor information, growth", combined with the measurement of production capacity, the calculation of the influence variables and the utilization rate of surplus production capacity in the forecasting period are carried out. In this paper, grey prediction of surplus production capacity of manufacturing enterprises based on electricity consumption is carried out. The causes of errors are analyzed. The grey prediction technology is improved by using abrupt data removal method and trend moving average method, and the final prediction model is established. A model for forecasting the surplus production capacity of the same type of enterprises based on electricity consumption is established. Finally, an example is given to verify the validity of the prediction model.
【學(xué)位授予單位】：寧波大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TH186
【參考文獻】
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