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 the study were to fine the survival probabilities the haazard rates of dropping out of the tecnical diploma level students at Rajamangala University of Technology phra Nakhon North Bangkok Campus and the factors influencing on the university in 2005. The study foud tht the frist semester the survival probabilities of students in all divisions were high ranging from 0.9210 to 1.00 For the second semester the survival probabilities of students in all divisions ranged from 0/7820 to 0.8815 except Industrial Technology which had a maximum value of 0.9295 aned Metal Technology which had aminimum value of 0.6271. For the third and the fourth semester there were 6 division which had survival probabilities ranging from 0.7142 to 0.7971. They were Tool and Die Making Computer technology Eletrical Technology Electronics Auromatic Machine and production design. They 4 were Mechanical Tool Technology Business Computer Mechanical Power and Industrial Technolog. the hazard rates of dropping of students in all divising were low ranging for 0.00 to 0.0822. For the second semester the hazard rates of dropping of students in all divisions ranged from 0.1260 to 0.2458 except Industrial technology which had a minimum value of 0.0730 and Metal Technology which had a miximum value of 0.5221. For the third and the fourth semester the hazard rates of students in all division ranged from 0.1355 to 0.2962 excepy metal Technology which had the maximum value of 0.5932 and Tool and Die Making which had the value of 0.3364. The factors influencing on hazard rates of students drpout were place of previous school region of previos school and grade point average of previons school. The hazard rate of dropping of students graduation from school in the central region (not including Bankok Metropolitan Area) was 43% (0.562 times) less than the hazard rate of dropping of students graduation from schools in Bangkok metropolitan area. The hazard rate of dropping of students having grade aveage of 3.10-3.50 and 3.51-4.00 from previous school was 43.1% (0.569 times) and 63.7% (0.377 times) less than the hazard rate of students having grade point average of 2.00-2.50 respectively.