• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.57-65
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• 2022

DEA(data envelopment analysis) is a technique for evaluation of relative efficiency of decision making units (DMUs) that have multiple input and output. A DEA model measures the efficiency of a DMU by the relative position of the DMU's input and output in the production possibility set defined by the input and output of the DMUs being compared. In this paper, we proposed several DEA models measuring the multi-period efficiency of a DMU. First, we defined the input and output data that make a production possibility set as the spanning set. We proposed several spanning sets containing input and output of entire periods for measuring the multi-period efficiency of a DMU. We defined the production possibility sets with the proposed spanning sets and gave DEA models under the production possibility sets. Some models measure the efficiency score of each period of a DMU and others measure the integrated efficiency score of the DMU over the entire period. For the test, we applied the models to the sample data set from a long term university student training project. The results show that the suggested models may have the better discrimination power than CCR based results while the ranking of DMUs is not different.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.4
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• pp.37-50
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• 2012

The existing DEA models have been devoted to evaluate relative efficiency of DMUs based on multiple input and output factors of a same period. However, a certain kind of lead time can be required to produce outputs using inputs in an organization. R&D evaluation is a typical area with this kinds of time lag. Thus, the purpose of this paper is to develop a new DEA model to deal with time lag effect in performance evaluation. The proposed model is to find relative efficiency of each DMU for each period considering the time lag effect. A case example using a real data set is also given to show the usage or implication of the suggested model. The results are compared with the ones of the CCR model and the multi-periods input model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.1-14
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• 2021

It is highly challenging to measure the efficiency of electric vehicle charging stations (EVCSs) because factors affecting operational characteristics of EVCSs are time-varying in practice. For the efficiency measurement, environmental factors around the EVCSs can be considered because such factors affect charging behaviors of electric vehicle drivers, resulting in variations of accessibility and attractiveness for the EVCSs. Considering dynamics of the factors, this paper examines the technical efficiency of 622 electric vehicle charging stations in Seoul using data envelopment analysis (DEA). The DEA is formulated as a multi-period output-oriented constant return to scale model. Five inputs including floating population, number of nearby EVCSs, average distance of nearby EVCSs, traffic volume and traffic congestion are considered and the charging frequency of EVCSs is used as the output. The result of efficiency measurement shows that not many EVCSs has most of charging demand at certain periods of time, while the others are facing with anemic charging demand. Tobit regression analyses show that the traffic congestion negatively affects the efficiency of EVCSs, while the traffic volume and the number of nearby EVCSs are positive factors improving the efficiency around EVCSs. We draw some notable characteristics of efficient EVCSs by comparing means of the inputs related to the groups classified by K-means clustering algorithm. This analysis presents that efficient EVCSs can be generally characterized with the high number of nearby EVCSs and low level of the traffic congestion.

• Journal of Distribution Science
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• v.13 no.4
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• pp.45-53
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• 2015

Purpose - Online shopping enables consumers to conveniently purchase products irrespective of the time and place. As a result, several online shopping companies have emerged to cater to this growing market and, therefore, the competition among them has become increasingly intense. This paper evaluates the comparative efficiency of online shopping companies for a multi-year period (2009-2013), in order to help online shopping managers identify major drivers for enhancing management efficiency and the subsequent competitiveness. Research design, data, and methodology - The researchers collected the data from 2009 to 2013 from the distribution yearbook. This paper analyzes the marketability (sales figures), profitability (business profits), and management conditions (net profits) of domestic online shopping enterprises by incorporating information on human resources (number of employees) and material resources (total assets and capital). Therefore, the number of employees, total assets, and capital are selected as input variables, and sales figures, business profits, and net profits as the output variables. In this study, Data Envelopment Analysis (DEA) was used to measure the comparative efficiency of domestic online shopping companies. In addition, the Malmquist Productivity Index was used to evaluate the trend of change of Decision Making Units' (DMUs') efficiency for a multi-year period. Results - First, as of 2013, Interpark (2.415) was found to be the most efficient online shopping enterprise, followed by Aladdin Communications (2.117), Hyundai Home shopping (1.867), Home&Shopping (1.176), NS Home shopping (1.170), Commerce Planet (1.126), CJ O Shopping (1.105), Ebay Korea (1.088), and GS Home Shopping (1.051). Second, this study recognizes how the management efficiency has changed for the period 2009-2013. Third, the lesser the capital and employees, the more are the net profits, and the better is the management efficiency of domestic online shopping companies. Lastly, the productivity of such companies is influenced by endogenous factors rather than exogenous factors such as shifts in business environment and technological advances. Conclusions - DHC Korea influenced various distribution channels to reach customers through the Internet. Consequently, this helped in increasing the awareness about its products, in addition to an increase in sales. These achievements can be attributed to the characteristics of online shopping companies. Although it is easy for these companies to suggest goods for one-off purchases, they however have difficulties in retaining customers. Overcoming this challenge can be one of the ways to benchmark a successful case of an efficient company. For example, an online shopping company can attract customers by developing a corresponding mobile application as a convenient way to shop online. Additionally, they can satisfy customers by quick delivery of purchased products, which is possible by building an effective logistics network. Our study indicates that the productivity of an online shopping company was influenced by endogenous factors driven by improvements in managerial practices rather than exogenous factors. Accordingly, online shopping companies should adopt strategies to improve their operational efficiency rather than sales volume-oriented management.

• Journal of Technology Innovation
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• v.11 no.2
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• pp.151-173
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• 2003

This paper analyses the efficiency and productivity of R&D system across time (1991${\sim}$2000) and 16 OECD countries using multi-output and multi-input non-parametric frontier methods such as DEA (data envelopement analysis) and Malmquist productivity indexes. Malmquist productivity indexes are decomposed into two components measures, namely technical change and efficiency change. To calculate R&D efficiency and productivity, we used R&D stock and the number or researchers as R&D input proxies and the number of adjusted SCI papers and U.S. patent applications as R&D output proxies. Empirical result shows that Switzerland, Canada, U.S., Australia's R&D efficiencies are the highest and Korea's R&D productivity growth is the highest in the sample for the period. Technical efficiency growth was a more important source of productivity growth than technological innovation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.8-14
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• 2019

Most of the data envelopment analysis (DEA) models evaluate the relative efficiency of a decision making unit (DMU) based on the assumption that inputs in a specific period are consumed to produce the output in the same period of time. However, there may be some time lag between the consumption of input resources and the production of outputs. A few models to handle the concept of the time lag effect have been proposed. This paper suggests a new multi-period input DEA model considering the consistent time lag effects. Consistency of time lag effect means that the time delay for the same input factor or output factor are consistent throughout the periods. It is more realistic than the time lag effect for the same output or input factor can vary over the periods. The suggested model is an output-oriented model in order to adopt the consistent time lag effect. We analyze the results of the suggested model and the existing multi period input model with a sample data set from a long-term national research and development program in Korea. We show that the suggested model may have the better discrimination power than existing model while the ranking of DMUs is not different by two nonparametric tests.

• Journal of Korea Water Resources Association
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• v.40 no.9
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• pp.687-696
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• 2007

Preference ordering approach is applied to optimize the parameters of Tank model using multi-objective genetic algorithm (MOGA). As more than three multi-objective functions are used in MOGA, too many non-dominated optimal solutions would be obtained thus the stakeholder hardly find the best optimal solution. In order to overcome this shortcomings of MOGA, preference ordering method is employed. The number of multi-objective functions in this study is 4 and a single Pareto-optimal solution, which is 2nd order efficiency and 3 degrees preference ordering, is chosen as the most preferred optimal solution. The comparison results among those from Powell method and SGA (simple genetic algorithm), which are single-objective function optimization, and NSGA-II, multi-objective optimization, show that the result from NSGA-II could be reasonalby accepted since the performance of NSGA-II is not deteriorated even though it is applied to the verification period which is totally different from the calibration period for parameter estimation.

• ETRI Journal
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• v.25 no.4
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• pp.211-218
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• 2003

This paper presents a dynamic resource allocation algorithm with multi-frequency time-division multiple access for the return link of interactive satellite multimedia networks such as digital video broadcasting return channel via satellite systems. The proposed timeslot assignment algorithm, called the very efficient dynamic timeslot assignment (VEDTA) algorithm, gives an optimal assignment plan within a very short period. The optimality and computational efficiency of this algorithm demonstrate that it will be useful in field applications.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.1
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• pp.1-9
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• 2009

The objective of this paper was to evaluate the auto-calibration with multi-objective optimization method to calibrate the parameters of the Soil and Water Assessment Tool (SWAT) model. The model was calibrated and validated by using nine years (1996-2004) of measured data for the 384-ha Baran reservoir subwatershed located in central Korea. Multi-objective optimization was performed for sixteen parameters related to runoff. The parameters were modified by the replacement or addition of an absolute change. The root mean square error (RMSE), relative mean absolute error (RMAE), Nash-Sutcliffe efficiency index (EI), determination coefficient ($R^2$) were used to evaluate the results of calibration and validation. The statistics of RMSE, RMAE, EI, and $R^2$ were 4.66 mm/day, 0.53 mm/day 0.86, and 0.89 for the calibration period and 3.98 mm/day, 0.51 mm/day, 0.83, and 0.84 for the validation period respectively. The statistical parameters indicated that the model provided a reasonable estimation of the runoff at the study watershed. This result was illustrated with a multi-objective optimization for the flow at an observation site within the Baran reservoir watershed.

• Journal of Mechanical Science and Technology
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• v.18 no.5
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• pp.865-876
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• 2004

A new concept of reduced quasi-dimensional combustion model for a direct injection diesel engine is developed based on the previously developed quasi-dimensional multi-zone model to improve the computational efficiency. In the reduced model, spray penetration and air entrainment are calculated for a number of zones within the spray while three zones with aggregated spray zone concept are used for the calculation of spray combustion and emission formation processes. It is also assumed that liquid phase fuel appears only near the nozzle exit during the breakup period and that spray vaporization is immediate in order to reduce the computational time. Validation of the reduced model with experimental data demonstrated that the new model can predict engine performance and NO and soot emissions reasonably well compared to the original model. With the new concept of reduced model, computational efficiency is significantly improved as much as 200 times compared to the original model.