PURPOSES : Construction cost estimates are important information for business feasibility analysis in the planning stage of road construction projects. The quality of current construction cost estimates are highly dependent on the expert's personal experience and skills to estimate the arithmetic average construction cost based on past cases, which makes construction cost estimates subjective and unreliable. An objective approach in construction cost estimation shall be developed with the use of machine learning. In this study, past cases of road projects were analyzed and a machine learning model was developed to produce a more accurate and time-efficient construction cost estimate in teh planning stage. METHODS : After conducting case analysis of 100 road construction, a database was constructed including the road construction's details, drawings, and completion reports. To improve the construction cost estimation, Mallow's Cp. BIC, Adjusted R methodology was applied to find the optimal variables. Consequently, a plannigs-stage road construction cost estimation model was developed by applying multiple regression analysis, regression tree, case-based inference model, and artificial neural network (ANN, DNN). RESULTS : The construction cost estimation model showed excellent prediction performance despite an insufficient amount of learning data. Ten cases were randomly selected from the data base and each developed machine learning model was applied to the selected cases to calculate for the error rate, which should be less than 30% to be considered as acceptable according to American Estimating Association. As a result of the analysis, the error rates of all developed machine learning models were found to be acceptable with values rangine from 17.3% to 26.0%. Among the developed models, the ANN model yielded the least error rate. CONCLUSIONS : The results of this study can help raise awareness of the importance of building a systematic database in the construction industry, which is disadvantageous in machine learning and artificial intelligence development. In addition, it is believed that it can provide basic data for research to determine the feasibility of construction projects that require a large budget, such as road projects.

The aim of this study is to establish an efficient distribution planning for a capacitated multi-stage supply chain. We assume that the demand information during planning horizon is given a deterministic form using a certain forecasting method. Under such a condition, we present a cost effective heuristic method for minimizing chain-wide supply chain inventory cost that is the sum of holding and backorder costs by using look-ahead technique. We cope with the capacity restriction constraints through look-ahead technique that considers not only the current demand information but also future demand information. To evaluate performance of the proposed heuristic method, we compared it with the extant research that utilizes echelon stock concept, under various supply chain settings.

The purpose of this study is to investigate factors influencing risk assessment of material misstatement in Vietnamese enterprises listed on stock market. Expert interview method was conducted to discover the scales for three variables including information system, trademark, and risk assessment of material misstatement. Survey method was used to examine the impacts of eight factors on risk assessment of material misstatement. Data is collected from 317 auditors who have excellent experience in auditing financial statements of companies listed on stock market. Then, data is processed by descriptive statistics, reliability analysis, factor extracted analysis, correlative regression analysis, and analysis variance of residual change. The research findings showed that business characteristic, stakeholder pressure, and economic environment have positive relationships with risk assessment of material misstatement. Three variables including operation control and monitor, control environment, and information system negatively affect to risk assessment. Specially, business characteristic and information system, which are elements in internal control, have strongest impact on risk assessment. One the other hand, assessment of internal control plays an important role not only in the audit plan stage but also throughout the stages of the audit implementation and ending. Therefore, appropriate solutions are proposed to carry out all audit stages.