Tourism can be described as the activities of visitors who make a visit to the main destination outside their usual environment for less than a year for any purpose. The tourism industry has become one of the influential sectors in global economic growth. Thus, tourism forecasting plays an important role in public and private sectors concerning future tourism flows. This study is an attempt to determine the best model in forecasting the international tourist's arrival in Malaysia based on Box-Jenkins and Holt-Winters model. The comparison of the accuracy of the techniques between Box-Jenkins SARIMA and Holt-Winters model was done based on the value of Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The secondary time series data were obtained from the Tourism Malaysia Department, which consists of a number of tourist arrivals from Singapore, Korea, and the United Kingdom from the year 2013 until the year 2017. The findings of this study suggest that the SARIMA and Holt-Winters model are suitable to be used in forecasting tourist arrivals. This study found that the Holt-Winters model is the appropriate model to forecast tourist arrivals from the United Kingdom (UK) and Korea. While SARIMA (1,1,1) (1,1,1)12 is the appropriate model for forecasting tourist arrivals from Singapore.

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Livestock feed blend formulation is an important process in livestock industry.  This process will help the livestock industry nowadays to keep providing continuous supply of animal protein food to cater for the expanding and increasing demand as Malaysia is undergoing a rapid growth in economic and human population.  The formulation of feed blend involves multiple objectives to be achieved through the decision making process.  In this project, Goal Programming (GP) method is used to formulate the livestock feed blend for a farm situated in Negeri Sembilan, Malaysia. This method is an approach of assisting the decision makers to solve multiple objectives for livestock feed blend in determining an optimal combination of ingredients to meet the nutritional requirements. This will lead to a rational use of available resources by minimizing the production cost and maximizing the nutritional value required for the growth of livestock. The nutrition for the livestock is dry matter (DM), metabolism energy (ME), crude protein (CP) and crude fiber (CF). Then, the preemptive model is tested using LINGO software and the results have been validated by using Mean Absolute Percentage Error (MAPE).  All of the multiple objectives have been fully achieved which represents the ability of the goal programming model to comply with optimizing the feed blend formulation.

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The incident rate has been widely used to indicate safety performance. The incident rate of a company can be compared with the national or international incident rate within similar  industry or among different type of industries. The comparison is particularly very useful as a safety benchmark to gauge performance with other companies in the same business area. However, many existing methods produce the annual incident rate, which has been formulated on an annual basis. This will lead to incompatibility of the method used in calculating the incident rate for a project that runs for a specific period. This is because the annual incident rate does not consider the duration of the project; it  becomes less meaningful in indicating the safety performance of project-based activities such as those in construction industries. The proposed method which is Project-Based Incident Rate (PIR) is found to be able to reflect the actual situation of project-based companies better than the existing annual incident rate method, and it is also can be expressed both on a monthly and yearly basis.