• 산업경영시스템학회지
• /
• 제38권3호
• /
• pp.159-168
• /
• 2015

Aggregate Production Planning determines levels of production, human resources, inventory to maximize company's profits and fulfill customer's demands based on demand forecasts. Since performance of aggregate production planning heavily depends on accuracy of given forecasting demands, choosing an accurate forecasting method should be antecedent for achieving a good aggregate production planning. Generally, typical forecasting error metrics such as MSE (Mean Squared Error), MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and CFE (Cumulated Forecast Error) are utilized to choose a proper forecasting method for an aggregate production planning. However, these metrics are designed only to measure a difference between real and forecast demands and they are not able to consider any results such as increasing cost or decreasing profit caused by forecasting error. Consequently, the traditional metrics fail to give enough explanation to select a good forecasting method in aggregate production planning. To overcome this limitation of typical metrics for forecasting method this study suggests a new metric, WACFE (Weighted Absolute and Cumulative Forecast Error), to evaluate forecasting methods. Basically, the WACFE is designed to consider not only forecasting errors but also costs which the errors might cause in for Aggregate Production Planning. The WACFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We demonstrate the effectiveness of the proposed metric by conducting intensive experiments with demand data sets from M3-competition. Finally, we showed that the WACFE provides a higher correlation with the total cost than other metrics and, consequently, is a better performance in selection of forecasting methods for aggregate production planning.

• 국제학술발표논문집
• /
• The 6th International Conference on Construction Engineering and Project Management
• /
• pp.72-76
• /
• 2015

Capital project benchmarking requires an effective cost normalization process to compare cost performance of projects accomplished in different time and location. Existing cost normalization approaches have been established based on the assumption that all required information for cost normalization is fully identified once a project is completed. Cost normalization, however, is sometimes required to evaluate phase-level outcomes of an ongoing project where the required information is not fully available. This paper aims to provide a cost normalization procedure for phase-based performance assessment. The procedure consists of three normalization steps: currency conversion, location adjustment, and time adjustment considering various scenarios where the required information is not fully identified. This paper also presents how the cost normalization procedure has been applied to the 10-10 Performance Assessment Program, which is a phase-based performance assessment system developed by the Construction Industry Institute (CII). Both researchers and industrial professionals can apply the cost normalization procedure to studies and practices regarding to cost estimation, feasibility analysis, and performance assessment.

• 국제학술발표논문집
• /
• The 9th International Conference on Construction Engineering and Project Management
• /
• pp.590-598
• /
• 2022

Benchmarking is an important tool to assess the performance of capital projects in the construction industry. Incorporating cost-related metrics into a benchmarking system requires an effective cost normalization process to enable meaningful comparisons among projects that were executed at different locations and times. Projects in the downstream and chemicals sector have unique characteristics compared to other types of construction projects, they require a distinctive cost normalization framework to be developed to benchmark their absolute cost performance. The purpose of this study is to develop such a framework to be used for the case of benchmarking the downstream and chemical projects for their performance assessment. The research team started with a review of existing cost normalization methodologies adopted in benchmarking systems and conducted 7 interviews to identify the current cost normalization practices used by industrial professionals. A panel of 12 experts was then convened and it held 6 review sessions to accomplish the framework development. The cost normalization framework for benchmarking downstream and chemical projects was established as a three-step procedure and it adopts a 4-element cost breakdown structure to accommodate projects submitted by both owners and contractors. It also incorporated 5 published cost indexes that are compatible with downstream and chemical projects and they were embedded into 2 options to complete the normalization process. The framework was then pilot-tested on 4 completed projects to validate its functional practicality and the downstream and chemical use case in the benchmarking system.

• 대한토목학회논문집
• /
• 제43권6호
• /
• pp.801-809
• /
• 2023

대규모로 진행되는 도로건설사업에서 비용 초과와 공기 지연이 지속적으로 발생하고 있지만 이를 사전에 관리하고 대비할 수 있는 성과평가 체계 연구는 미흡한 실정이다. 또한 긴 공기를 가진 도로건설사업은 시공과정에서 많은 요인들에 영향을 받기 때문에 도로만의 특성을 고려한 성과 평가를 실시하고, 추후 유사한 사업에 대비할 필요가 있다. 따라서 본 연구는 도로건설사업의 시공단계 성과평가 프레임워크를 개발하여 도로건설사업의 성과관리 방안을 제시하고자 한다. 본 연구는 각종 유관기관에서 도로건설사업 시공단계의 정보를 수집하고 속성정보에 따라 데이터베이스를 구축하였다. 또한 사업의 착공·준공시점 간 시간차에 대해 비용 표준화를 실시하였고, 성과평가를 위한 지표를 도출하여 분석을 실시하였다. 본 연구는 도로건설사업 시공과정의 절대적, 상대적 비용·일정 성과를 정량적으로 분석함으로써 성과평가 연구의 고도화 가능성과 신규 도로건설사업 계획 시 활용방안을 보여준다.

• Structural Monitoring and Maintenance
• /
• 제6권3호
• /
• pp.219-235
• /
• 2019

Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.

• 한국건설관리학회논문집
• /
• 제24권1호
• /
• pp.3-11
• /
• 2023

현재 국내에서는 건설공사 사후평가 제도에 따라 사업 준공 후 공사내용 및 그 효과를 조사하여 프로젝트의 성과를 평가하고 향후 계획된 유사 건설사업의 효율적인 수행을 위한 자료로 활용하고 있다. 그러나 건설사업의 성과를 보다 효과적으로 관리하기 위해서는 프로젝트 진행 과정에서 단계별 성과를 평가하여 그에 따른 유연한 관리전략을 수립할 수 있어야 한다. 따라서 건설사업의 효율적인 성과관리를 위해서는 건설사업 단계별 성과평가체계가 필요하다. 본 연구는 건설사업 초기단계의 성과를 평가할 수 있는 계획단계 벤치마킹을 위한 기초 모델을 제시하고자 한다. 이를 위하여 본 연구에서는 한국개발연구원에서 제공하고 있는 예비타당성 조사 보고서 중 완료된 도로공사 354건에 대해 데이터베이스를 구축하였다. 계획단계 성과를 평가할 성과지표로 시설용량을 기준으로 한 표준공사비와 표준공사기간 지표를 개발하여 도로사업의 계획단계의 성과를 분석할 수 있는 벤치마킹 모델을 제시하였다. 이를 통해 도로사업 계획단계의 표준공사비 및 표준공사기간을 산정하고, 도로사업의 특성별 차이를 분석하였다. 본 연구의 결과를 활용하여 계획단계의 성과와 설계 및 시공단계의 성과를 연계하여 분석한다면 건설사업 생애주기 동안의 성과관리가 가능할 것으로 기대된다.

• Steel and Composite Structures
• /
• 제52권2호
• /
• pp.145-163
• /
• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.