• 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 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.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 Korean Institute of Industrial Engineers
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• v.11 no.2
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• pp.47-56
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• 1985

This paper considers a two-product three-facility production planning model, where facility 1 produces product 1 to satisfy its own market requirements and supplies input to facility 2, and facility 2 requires another input from facility 3 (outside supplier). The objective is to determine the optimal production amount in each period in order to satisfy the dynamic demands on time, which minimizes the total cost of production and storage. The set-up cost is incurred jointly from the multi-facility operations.

• Journal of Power Electronics
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• v.12 no.6
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• pp.912-924
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• 2012

In hybrid renewable power systems, the use of a multiple-input dc/dc converter (MIC) leads to simpler circuit and lower cost, when compared to the conventional use of several single-input converters. This paper proposed a novel three-input buck/boost/buck-boost converter, which can be used in applications with various values of input voltage. The energy sources in this converter can deliver power to the load either simultaneously or individually in one switching period. The steady relationship, the power management strategy and the small-signal circuit model of this converter have been derived. With decoupling technology, modeling and regulator design can be obtained under multi-loop control modes. Finally, three generating methods of a multiple-input buck/boost/buck-boost converter is given, and this method can be extended to the other multiple-input dc/dc converters.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.1-6
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• 1993

This report proposes a novel method of identification of continuous time-delay systems from sampled input-output data. By the aid of a digital pre-filter, an approximated discrete-time estimation model is first derived, in which the system parameters remain in their original form and the time delay need not be an integral multiple of th sampling period. Then an identification method combining the common linear least squares(LS) method or the instrumental variable(IV) method with the genetic algorithm(GA) is proposed. That is, the time-delay is selected by the GA, and the system parameters are estimated by the LS or IV method. Furthermore, the proposed method is extended to the case of multi-input multi-output systems where the time-delays in the individual input channels may differ each other. Simulation resutls show that our method yields consistent estimates even in the presence of high measurement noises.

• Asia pacific journal of information systems
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• v.7 no.1
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• pp.67-83
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• 1997

The utility of learning techniques in investment analysis has been demonstrated in many areas, ranging from forecasting individual stocks to entire market indexes. To date, however, the application of artificial intelligence to financial forecasting has focused largely on short predictive horizons. Usually the forecast window is a single period ahead; if the input data involve daily observations, the forecast is for one day ahead; if monthly observations, then a month ahead; and so on. Thus far little work has been conducted on the efficacy of long-term prediction involving multiperiod forecasting. This paper examines the impact of alternative procedures for extended prediction using knowledge discovery techniques. One dimension in the study involves temporal granularity: a single jump from the present period to the end of the forecast window versus a web of short-term forecasts involving a sequence of single-period predictions. Another parameter relates to the numerosity of input variables: a technical approach involving only lagged observations of the target variable versus a fundamental approach involving multiple variables. The dual possibilities along each of the granularity and numerosity dimensions entail a total of 4 models. These models are first evaluated using neural networks, then compared against a multi-input jump model using case based reasoning. The computational models are examined in the context of forecasting the S&P 500 index.

• Journal of the Korean earth science society
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• v.42 no.4
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• pp.445-458
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• 2021

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

• Economic Analysis
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• v.26 no.4
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• pp.65-97
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• 2020

The purpose of this paper is to analyze the effects of exports on Korea's employment and to decompose driving factors of change in Korea's employment embodied in exports (EEX). This study uses a multi-regional input-output (MRIO) and structural decomposition analysis (SDA) for empirical analysis, and uses a dataset of World Input-Output Tables (WIOTs) and Socio-Economic Accounts (SEAs) from the World Input-Output Database (WIOD). The main findings of the empirical results are summarized as follows. First, Korea's EEX continues to increase and Korea's share of EEX compared to total employment shows an upward trend. However, Korea's employment inducement coefficient of value-added exports showed a downward trend during the 2000-2014 period. Second, final demand from three countries (China, the United States, and the Rest of the World (RoW)) has affected a significant portion of Korea's EEX. Finally, from the results of the SDA, the effect of changes in final demand was the most important driving factor for the increase in Korea's EEX. Based on the results of this empirical analysis, this study discusses useful policy implications that could increase domestic employment in Korea.

• IE interfaces
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• v.13 no.2
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• pp.217-224
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• 2000

This paper analyzes the effects of setup cost reduction in a dynamic lot-sizing model for a single-facility multi-product problem. In the model, demands for each product are known, no backlogging is allowed, and a single resource is employed. Also, setup cost is defined as a function of capital expenditure to invest in setup cost reduction. Furthermore, in each production period the facility (or plant) produces many products, each representing a fixed part of the involved production activity (or input resource quantity). In this paper, the structure of the optimal solution is characterized and an efficient algorithm is proposed for simultaneously determining the optimal lot size with reduced setup cost and the optimal investment in setup cost reduction. Also, the proposed algorithm is illustrated by a numerical example with a linear and an exponential setup reduction functions.