The objective of this research is to propose a forecasting model for uncertain demand index data of steel. The forecasting model proposed by Yelland (2010) was modified by adjusting the prior distributions of some parameters in the model in order that it is suitable for the demand of steel in Thailand. The algorithms for model fitting were written in OpenBUGS. The proposed model was compared to a classical exponential smoothing model. The research found that the forecast errors from the proposed forecasting model were minimum.

The objectives of this research were to select the most appropriate forecasting model for uncertain demand of hot-rolled steel and to find the purchase amount and the reorder point of some raw materials in order to minimize the total cost of production in an iron and steel production planning. The three forecasting models which were exponential model ARIMS model and the proposed Bayesian model were studied. An iron and steel factory in Samutprakarn province was a prototype for this study. Because of their uncertain high demand the Steel H-Beam Steel plate steel round bar and black steel round bar with various sizes were selected and their parameters including demand rate inventory cost back order cost and setup cost were collected. The study found that the proposed Bayesian forecasting model is the most appropriate. The predicted demand and all parameter values were used to find the purchase amounts and the reorder points of the raw materials under the (rQ) policy of the stochastic inventory model.

The objectives of this research were to find the distribution of food demand for laying hens and to find the optimum amount of that food production which had minimum cost. The data consisted of customer purchase orders for the food of laying hens price of food for laying hens cost per unit for the food inventory cost related to food of laying hens in which the food was out of stock such as fine overtime urgent purchase for material. They were collected in 2008-2009 form a factory in Nakhonratchasima province. The collected data were analized in order to explore the distribution of the monthly food demand for the laying hens and to see the rate of inventory per unit. The results were used in the stochastic linear programming model for aggregate planning in which the optimum production minimum cost could be obtained. Programming algorithm in MATLAB and tools in Linprog software were used to get the solution. The distribution of the food demand for laying hens and the random numbers were used in the model. The study found that the distribution of food demand for laying hens every month has normal distribution (January to December). The monthly average amount of production from January to December were 10936.94 12913.74 11898.12 13096 15443.26 14916.33 10322.64 10818.21 11393.11 11700.72 9706.05 and 11702.05 respectively. The minimum total cost average for 12months was Baht 62422386.98.

The objectives of this research were to find the distribution of food demand for laying hens and to find the optimum amount of that food production which had minimum cost. The data consisted of customer purchase orders for the food of laying hens price of food for laying hens cost per unit for the food inventory cost related to food of laying hens in which the food was out of stock such as fine overtime urgent purchase for material. They were collected in 2008-2009 form a factory in Nakhonratchasima province. The collected data were analized in order to explore the distribution of the monthly food demand for the laying hens and to see the rate of inventory per unit. The results were used in the stochastic linear programming model for aggregate planning in which the optimum production minimum cost could be obtained. Programming algorithm in MATLAB and tools in Linprog software were used to get the solution. The distribution of the food demand for laying hens and the random numbers were used in the model. The study found that the distribution of food demand for laying hens every month has normal distribution (January to December). The monthly average amount of production from January to December were 10936.94 12913.74 11898.12 13096 15443.26 14916.33 10322.64 10818.21 11393.11 11700.72 9706.05 and 11702.05 respectively. The minimum total cost average for 12months was Baht 62422386.98.

The objectives of this research are: to study the characteristics of students in the Faculty of Engineering at Rajamangala University of Technology Pra Nakorn North Bangkok Campus that influence to the risk of withdrawal from Calculus 2 for Engineers; to predict the risk of withdrawal from Calculus 2 for Engineers; and to use the results of the study for planning and solving the problems of withdrawal from Calculus 2 for Engineers. The sample is 226 students in the Faculty of Engineering at Rajamangala University of Technology who enrolled the Calculus 2 for engineers in the second semester of the academic year 2009. The data are from the questionnaire. The factors considered are grad point average the number of enrollments for the Calculus 1 for engineers Calculus 1 for Engineers grade the number of courses that emphasize on calculation that were enrolled in the second semester of the academic year 2009 the sacrifices of students for the Calculus 2 for Engineers course and the need to withdraw from Calculus 2 for Engineers course before other courses. The logistic regression model in which the parameters are estimated by using Bayesian methods is used for analyzing the data. The study shows that the factors that influence to the risk of withdrawal from the Calculus 2 for Engineers are grade point average Calculus 1 for Engineers grade the number of courses that emphasize on calculation that were enrolled in the second semester of the academic year 2009 and the sacrifices of students for the Calculus 2 for Engineers and the need to withdraw from Calculus 2 for Engineers before other courses. The factor that does not influence to the risk of withdrawal from the Calculus 2 for Engineers is the number of enrollments for the Calculus 1 for Engineers.