• Journal of Distribution Science
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• v.15 no.6
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• pp.37-46
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• 2017

Purpose - The paper aims to propose an optimization model for supporting the buyer-seller negotiations. We consider the price, quality, and delivery as evaluation criteria, also recognized as objectives for negotiation. Research design, data, and methodology - The methodology used in this paper involves the input-oriented DEA with the inverse optimization. Under the existence of several potential suppliers, the price would be considered to be the decision variable to conclude the negotiation so as to meet the desired level of the quality and delivery. The data set for six suppliers with three criteria is examined by the proposed approach. Results - We present the decision aid model by displaying the price spectrum as the changes of desired output levels. It overcomes the shortcomings from previous researches mainly based on the discrete types of scenario generations. This approach shows that the obtained results help the buyer understand the trade-offs between price and performance when he/she considers the negotiation. Conclusions - The paper contributes to the numerical models for buyer-supplier negotiation in that the model for the supplier evaluation and selection is closely linked with the model for negotiation. In addition, it eliminates the unrealistic negotiation strategy, and provides the negotiation strategies that the buyer would not shift the burden on suppliers by maintaining the current efficiency.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.534-541
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• 2011

Risk evaluation approaches for bidding on international construction projects are typically partitioned into three stages: country selection, project classification, and bid-cost evaluation. However, previous studies are frequently under attack in that they have several crucial limitations: 1) a dearth of studies about country selection risk tailored for the overseas construction market at a corporate level; 2) no consideration of uncertainties for input variable per se; 3) less probabilistic approaches in estimating a range of cost variance; and 4) less inclusion of covariance impacts. This study thus suggests a three-staged risk evaluation model to resolve these inherent problems. In the first stage, a country portfolio model that maximizes the expected construction market growth rate and profit rate while decreasing market uncertainty is formulated using multi-objective genetic analysis. Following this, probabilistic approaches for screening bad projects are suggested through applying various data mining methods such as discriminant logistic regression, neural network, C5.0, and support vector machine. For the last stage, the cost overrun prediction model is simulated for determining a reasonable bid cost, while considering non-parametric distribution, effects of systematic risks, and the firm's specific capability accrued in a given country. Through the three consecutive models, this study verifies that international construction risk can be allocated, reduced, and projected to some degree, thereby contributing to sustaining stable profits and revenues in both the short-term and the long-term perspective.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.51-59
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• 2017

Due to the rapid change of global business environment, the growth of China's steel industry and the inflow of cheap products, domestic steel industry is faced on downward trend. The change of business paradigms from a quantitative growth to a qualitative product is needed in this steel industry. In this environment, it is very important for domestic steel distribution companies to secure their competitiveness by selecting good supply companies through a efficient procurement strategy and effective method. This study tried to find out the success factors of steel distribution industry based on survey research from experts. Weighted values of each factors were found by using AHP (analytic hierarchy process) analysis. The weighted values were applied to DEA(data envelopment analysis) model and eventually the best steel supply company were selected. This paper used 29 domestic steel distribution firms for case example and 5 steps of decision process to select good vendors were suggested. This study used quality, price, delivery and finance as a selection criteria. Using this four criterions, nine variable were suggested. Which were product diversity, base price, discount, payment position, average delivery date, urgency order responsibility and financial condition. These variables were used as a output variable of DEA. Sales and facilities were used as an input variable. Pairwise comparison was conducted using these variables. The weighted value calculated by AHP pairwise comparison were used for DEA analysis. Through the analysis of DEA efficiency process, good DMU (decision making unit) were recommended as a steel supply company. The domestic case example was used to show the effectiveness of this study.

• Smart Structures and Systems
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• v.13 no.4
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• pp.711-730
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• 2014

In this paper the system dynamic influences in actuators with variable stiffness as contemporary used in robotics for safety and efficiency reasons are investigated. Therefore, different configurations of serial and parallel elasticities are modeled by dynamic equations and linearized transfer functions. The latter ones are used to identify the characteristic behavior of the different systems and to study the effect of the different elasticities. As such actuation concepts are often used to reach energy-efficient operation, a power consumption analysis of the configurations is performed. From the comparison of this with the system dynamics, strategies to select and control stiffness are derived. Those are based on matching the natural frequencies or antiresonance modes of the actuation system to the frequency of the trajectory. Results show that exclusive serial and parallel elasticity can minimize power consumption when tuning the system to the natural frequencies. Antiresonance modes are an additional possibility for stiffness control in the series elastic setup. Configurations combining both types of elasticities do not provide further advantages regarding power reduction but an input parallel elasticity might enable for more versatile stiffness selection. Yet, design and control effort increase in such solutions. Topologies incorporating output parallel elasticity showed not to be beneficial in the chosen example but might do so in specific applications.

• Journal of Korean Society of Rural Planning
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• v.26 no.2
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• pp.13-24
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• 2020

This study analyzed the management efficiency of local food stores that are increasing recently. The analysis targeted 25 local food outlets in Jeonbuk area, and the analysis method analyzed the efficiency by CCR model. The input variables used to analyze the input-oriented efficiency of local food stores are business expenses, employees, organizational number of participating farms, and number of items, and sales are used as output variables. The main contents of the analysis are as follows. First, local food outlets increased due to support projects such as the government, local governments, and agricultural cooperatives, but their dependence was high. Second, the management efficiency of 25 local food stores in Jeonbuk is 28.0% when the efficiency is 1.000, and 72.0% of inefficient local food stores. Third, considering the projection point and the reference group, there was room for improvement in input variables. Therefore, it was determined that improvement efforts are needed to secure the continuity of local food outlets in the future. However, this study will require review of variable selection and analysis methods for further analysis.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.141-149
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• 2008

Recently we frequently analyze multivariate data with quite large number of variables. In such data sets, virtually duplicated variables may exist simultaneously even though they are conceptually distinguishable. Duplicate variables may cause problems such as the distortion of principal axes in principal component analysis and factor analysis and the distortion of the distances between observations, i.e. the input for cluster analysis. Also in supervised learning or regression analysis, duplicated explanatory variables often cause the instability of fitted models. Since real data analyses are aimed often at multiple purposes, it is necessary to reduce the number of variables to a parsimonious level. The aim of this paper is to propose a practical algorithm for selection of a subset of variables from a given set of p input variables, by the criterion of minimum trace of partial variances of unselected variables unexplained by selected variables. The usefulness of proposed method is demonstrated in visualizing the relationship between selected and unselected variables, in building a predictive model with very large number of independent variables, and in reducing the number of variables and purging/merging categories in categorical data.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1141-1144
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• 2009

인공신경망(artificial neural network) 기법은 인간의 두뇌 신경세포의 활동을 모형화한 것으로 오랜 시간동안 발전해 왔으며 여러 분야에서 활용되고 있고 수문분야에서도 인공신경망을 이용한 연구가 활발히 진행되어 왔다. RDAPS와 같은 단기수치예보 자료는 강우의 유무 판단과 같은 정성적인 분석에서 비교적 정확도가 높지만 정확한 강우량의 추정과 같은 정량적인 부분에서는 정확도가 매우 낮으므로 인공신경망 기법과 같은 후처리 기법을 통해서 정확도를 높이게 된다. 인공신경망 기법을 수행할 때, 가장 중요한 것은 입력변수선택(input variable selection)으로 입력 변수의 적절한 선택이 결과값에 큰 영향을 주게 된다. 본 연구에서는 mutual information을 입력 변수 선택 기법으로 채택하여, 인공신경망의 입력변수 선정의 정확도를 알아보고자 한다. Mutual information은 주어진 자료의 엔트로피값을 이용하여 변수들 간의 독립과 종속의 관계를 나타내는 기법으로서, MI값은 '0'에서 '1'의 값을 가지며 '0'에 가까울수록 변수들 간의 관계가 독립적이고 '1'에 가까울수록 종속적인 관계를 나타낸다. 인공신경망의 입력변수선정에 대한 mutual information의 정확도를 알아보기 위해, 기존 입력변수선택 기법과 mutual information을 이용했을 경우의 인공신경망의 처리능력, 정확도를 비교 검토하였다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.4
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• pp.101-111
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• 2013

In this study, DEA (Data Envelopment Analysis) was carried out on the four bridges, which have the same extension (L=1,615m), in order to select the most superior, economical method of construction using the LCC concept of each bridge structure in the case of the Ulsan-Pohang double track railway which is scheduled to be constructed. DEA models were analyzed with the CCR model, which was designed for the evaluation of relative efficiency of each model. The initial construction costs, maintenance costs, indirect costs (user costs + indirect loss of social costs), and life cycle costs were used as input variables, and average duration was applied as the output variable. LCC was applied to calculate the input variables, and to get the costs of LCC, 100 years of period and 4.83% of real discount rate were applied, and the costs are classified into initial construction, maintenance, user, and indirect loss of social cost. The analysis results showed that the Method 2 and 3 were evaluated as the most efficient, and the other alternatives were evaluated as the following order; Method 1, the default, and Method 4.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3563-3571
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• 2011

In this research, we analyzed the efficiency of 20 public health centers of Gyeongnam Province during 2007-2009, so weakness of input and output factor were found. We used the CCR, BCC model of Data Envelopment Analysis as a method of evaluation, made a choice human resource as the input variable, made a selection the performance of public health care center, ward as the output variable. The results of this study show that the efficiency of 20 public health centers have got better because Government and Gyeongnam have provided administrative, financial support. It is expected that this research can give good information for effective management of public health centers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2129-2137
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• 2010

In this research, we analyze the efficiency of 20 public health centers of Gyeongnam Province, so the reduction and weakness of input and output factor were found. We used the CCR, BCC model of Data Envelopment Analysis as a method of evaluation, made a choice human resource as the input variable, made a selection the performance of health care center, ward as the output variable. The results show that 12(60%) public health centers in 20 were productive with respective to overall Technical Efficiency(average score 0.868), 14(70%) with respective to overall Pure Technical Efficiency(average score 0.924) and 12(60%) with respective to overall Scale Efficiency(average score 0.933). It is expected that this research can provide a good data for effective management of public health centers.