• Journal of Environmental Impact Assessment
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• v.9 no.4
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• pp.363-372
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• 2000

For any particular development project or environmental regulations, decision-making criteria is required and conflicts among criteria should be resolved. It is necessary to investigate criteria that government agencies employ in making decisions that influence the environment. The evaluation of alternative development proposals and regulatory measures involves much more than environmental issues. Economic, technical, and social factors should be considered along with environmental impacts when making evaluations. Evaluation should be based on values of all individuals who may be affected by public or private decisions. There are many evaluation methods for determining how individuals and groups value alternative public actions. Numerous weighting-scaling methodologies can be used in such evaluations. These methodologies represent adaptations of multiple-criteria or multiple-attribute decision-making techniques. Environmental risk assessment which accounts for uncertainties in choosing among alternative policies and projects is increasingly used.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.22-25
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• 2003

It is usually difficult for a single user to have all the essential knowledge on various Rapid Prototyping processes and techniques. It is therefore necessary to capture knowledge and experience of users of expert level into a decision-support system which provides quicker and more interactive way to select proper RP process and/or machine. rather than reading reports on benchmarking studies and comparing tables and graphs. In this paper two algorithms are presented, which may be used in such a decision-support system. together with its applications. The one is an extended PRES(Project Evaluation and Selection) algorithm which applies weighting factors of each attribute. The other is a LCE(Linear Confidence Equation) algorithm which is proposed to apply user's input requirements as well as weighting factors.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.1-20
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• 2008

Based on the multi-attribute utility theory (MAUT), we present a survey method to measure consumer preferences. The multi-attribute utility theory has been used to make decisions in OR/MS field; however, we show that the method can be effectively used to estimate the demand for new services by measuring individual level utility function. Because conjoint method has been widely used to measure consumer preferences for new products and services, we compare the pros and cons of two consumer preference survey methods. Further, we illustrate how swing weighing method can be effectively used to elicit customer preferences especially for new telecommunications services, Multi-attribute utility theory is a compositional approach for modeling customer preference, in which researchers calculate overall service utility by summing up the evaluation results for each attribute. On the contrary, conjoint method is a decompositional approach, which requires holistic evaluations for profiles. Partworth for each attribute is derived or estimated based on the evaluation, and finally consumer preferences for each profile are calculated. However, if the profiles are quite new and unfamiliar to the survey respondents, they will find it very difficult to accurately evaluate the profiles. We believe that the multi-attribute utility theory-based survey method is more appropriate than the conjoint method, because respondents only need to assess attribute level preferences and not holistic assessment. We chose swing weighting method among many weight assessment methods in multi-attribute utility theory, because it is designed to perform in a simple and fast manner. As illustrated in Clemen and Reilly (2001), to assess swing weights, the first step is to create the worst possible outcome as a benchmark by setting the worst level on each of the attributes. Then, each of the succeeding rows "swings" one of the attributes from worst to best. Upon constructing the swing table, respondents rank order the outcomes (rows). The next step is to rate the outcomes in which the rating for the benchmark is set to be 0 and the rating for the best outcome to be 100, and the ratings for other outcomes are determined in the ranges between 0 and 100. In calculating weight for each attribute, ratings are normalized by the total sum of all ratings. To demonstrate the applicability of the approach, we elicited and analyzed individual-level customer preference for new telecommunication services-WiBro and HSDPA. We began with a randomly selected 800 interviewees, and reduced them to 432 because other remaining ones were related to the people who did not show strong intention for subscription to new telecommunications services. For each combination of content and handset, number of responses which favored WiBro and HSDPA were counted, respectively. It was assumed that interviewee favors a specific service when expected utility is greater than that of competing service(s). Then, the market share of each service was calculated by normalizing the total number of responses which preferred each service. Holistic evaluation of new and unfamiliar service is a tough challenge for survey respondents. We have developed a simple and easy method to assess individual level preference by estimating weight of each attribute. Swing method was applied for this purpose. We believe that estimating individual level preference will be quite flexibly used to predict market performance of new services in many different business environments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.248-251
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• 1997

In this paper, We propose the concept of approximate Classification in the field of two group discriminan analysis. In our approach, an attribute space is divided into three subspaces. Two subspaces are for given two group and one subspace is for a boundary area between the two groups. We propose Approximate Pattern Classification with Rough set. We also propose learning procedures of neural networks for approximate classification. We propose two weighting methods which lead to possibility analysis and necessity analysis. We illustrate the proposed methods by numerical examples.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.3
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• pp.25-35
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• 2011

Traffic volume increases due to diversification of industry. Also, Automobile ownerships also increase steadily. It is estimated that the registered number of vehicle is expected to be 20 milion in the year 2015. These trends may result in increasing the number of woman drivers and elderly drivers. Therefore, this study aims to identify routes that reflect characteristics of each driver's preferences. A survey was conducted on different routes attributes for variances drivers. Driver types were classified by gender, age, and driving career. Accordingly, a weight for road composition attribute such as number of lanes, number of accidents, slope was estimated by using Swing Weighting technique in Multi-Attribute Utility Theory. In addition, a case study was conducted and identified weights were applied to routes. In result, drivers commonly prefer short route when they considered their routes. Also, male drivers prefer speedy and shorter route than that of female drivers. Elderly drivers prefer safe routes that represent low accidents rate. Moreover driving career under a year drivers prefer safe and easy routes. Therefore, we may conclude that the necessity of diversified route information is essential in the future car navigation system.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.3
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• pp.13-26
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• 2021

Recent drought events in the South Korea and the magnitude of drought losses indicate the continuing vulnerability of the agricultural drought. Various studies have been performed on drought hazard assessment at the regional scales, but until recently, drought management has been response oriented with little attention to mitigation and preparedness. A vulnerability assessment is introduced in order to preemptively respond to agricultural drought and to predict the occurrence of drought. This paper presents a method for spatial, Geographic Information Systems-based assessment of agricultural drought vulnerability in South Korea. It was hypothesized that the key 14 items that define agricultural drought vulnerability were meteorological, agricultural reservoir, social, and adaptability factors. Also, this study is to analyze agricultural drought vulnerability by comparing vulnerability assessment according to weighting method. The weight of the evaluation elements is expressed through the Analytic Hierarchy Process (AHP), which includes subjective elements such as surveys, and the Entropy method using attribute information of the evaluation items. The agricultural drought vulnerability map was created through development of a numerical weighting scheme to evaluate the drought potential of the classes within each factor. This vulnerability assessment is calculated the vulnerability index based on the weight, and analyze the vulnerable map from 2015 to 2019. The identification of agricultural drought vulnerability is an essential step in addressing the issue of drought vulnerability in the South Korea and can lead to mitigation-oriented drought management and supports government policymaking.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.135-151
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• 1997

In this paper, we propose a simple and relatively efficient heuristic method to determine job sequencing in the flow-shop considering multiple criteria such as processing time, due date and cost. The proposed method is applicable to the flow- shop where the jobs are released simultaneously and their processing sequence is predetermined and not changed until the whole jobs are processed. To develop this method, we mixed and modified some well-known multi-attribute decision heuristics such as the simple linear weighting scheme, the lexicographic rule and the 'elimination by aspect' rule. Some computer simulations were conducted to test the efficiency of the proposed method and it has been compared with the SWPT (Shortest Working Processing Time) rule and EDD (Earliest Due Date) rule. The results show that our method is as effective as the traditional ones in terms of mean flow time, tardiness, makespan, cycle time, machine utilization, etc., and proved to be much simpler and more flexible to be used in real situations.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.219-223
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• 2001

Within a mechanical system such as an automotive the number of standard machine parts is increasing, so that the parts selection becomes more important than ever before. Selection of appropriate bearings in the preliminary design phase of a machine is also important. In this paper, three decision-making approaches are compared to find out a model that is appropriate to bearing selection problem. An artificial neural network, which is trained with real design cases, is used to select a bearing mechanism at the first step. Then, the subtype of the bearing is selected by the weighting factor method. Finally, types of peripherals such as lubrication methods are determined by a rule-based expert system.

• Korean Management Science Review
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• v.20 no.1
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• pp.125-139
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• 2003

City gas is becoming an essential resource in households due to its convenience and low price in comparison to other energy sources. However, in order to prepare for the gas market to be saturated, many city gas companies must pursue customer satisfaction management and develop happy call systems for a promising solution. In the development of the happy call systems, the difficult problem is that we cannot contact all customers because of limited resources and calling efficiency in the call center, We should find best customers according to their value. This paper suggests a methodology for the selection of happy call customers when city 9as companies consider two strategies. First, they should launch a new business area. Second, they must attempt to prevent current customers from moving from city gas to other fuels. We will discover important attributes and derive rules of weighting for the attributes through an exploration study that affect customer satisfaction and preference. Through a simulation model, we will show how many customers will be selected by our methodology.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.2
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• pp.55-61
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• 2003

The purpose of this paper is to propose a method for selecting the preferred alternatives of Multiple- Attribute Decision-Making(MADM) problem using the Entropy measure. A decision-maker who wants to estimate exactly the weight to be applied to her/his MADM problem is usually confronted with the embarrassing situation where, although there exist a variety of weighting methods, it is hard to find a right procedure to choose a pertinent value To remedy this uncomfortable situation, the Entropy measure commonly used in information theory, Is proposed as a tool that can be used by decision-makers to more efficiently select the preferred alternatives. As a result, the method proposed in the paper can be significant in that relatively easy to understand by decision-makers.