컴퓨터 프로그램에 의한 의사결정분석

李昌孝 新羅大學校 1984 論文集 Vol.16 No.-

The aim of decision analysis is to try to supply some aids to the process of decision-making especially where the decision situation involves uncertainty. The idea is that, by proving the analytical framework, the manager will be able to concentrate on supplying the human inputs of intuition and experience one by one withut having to try to juggle these in his head, while at the same time tryinjg to provide further inputs. in this paer, the author concentrated on developing the computer program of Bayesian decision procedure with data and the procedure of computing the expected payoff with continuous variables. This computer program was written in Apple Ⅱ BASIC language and the author used the TriGem020 micro-computer with 48K memory.

부산지역 전략산업 선정에의 계층화 분석과정 적용

이창효 한국경영과학회 1999 經營 科學 Vol.16 No.1

AHP is a well-known multiattribute decision technique that has been applied to private and public areas it is well suited to group decision making and offers many benefits as a synthesizing mechanism in group decisions This paper explains why the AHP is well-suited to group decision making by means of the case study Through this case study, this paper tried setting priorities for the strategic industry with the focus on the small and medium industry in Pusan

多目的線型計劃問題에서의 選好解 探索

李昌孝 新羅大學校 1985 論文集 Vol.19 No.-

Among the preferred solution searching methods in multiobjective linear programming problems, this paper reviews in the first place, two representative methods, namely, the interactive subgradient method and the SWT method. Next, the interactive constraint method is presented by the author, as an aid in solving multiobjective linear programming problems. The advantages or characteristics of this method are following: (1) We can omit the redundant objective functions, because they do not affect the efficient solutions set. (2) Instead of providing an ordinal ranking to each of its objectives, this method needs only selecting the least important objective and this objective selected would be in the objective function as the primary objective. (3) Based on Simon`s theory of satisficing, this method determines the preferred solution or satisficing solution that is close to the minimum satisfactory level of the DM. (4) The DM is reguired only to provide yes or no answers to questions regarding the desirability of increase or decrease in objectives function values of solutions that he will not accept as optimal, i.e., not unduly demanding of the DM. (5) To obtain the preferred solution, this method requires only ε-levels which reflects the DM`s minimum satisfaction levels for each objective and the adjustment of this levels in the course of the analysis. (6) A LP optimization routing, i.e., the standard simplex algorithm could be used.

對話型 多 屬性效用分析에 관한 硏究

李昌孝 新羅大學校 1988 論文集 Vol.25 No.-

The multiattribute utility theory has been developed for handling uncertainty in outcomes. While there are many decision making models under certainty, only utility function models take into account the decision maker's risk preferences as well as the actuarialrelationship between activities and outcomes. And all utility function assessment techniuqes produce cleary defined statements of the decision maker's preferences. But, multiattribute utility theoryis one of the most confusing topics of multiattribute decision making study mainly due to its sophisticated nature(assumption) and the difficulty in the assessment of utility function. Futher more most of the literature is filled with mathematical proofs. Therefore there remains a certain amount of skepticism concerning the practical usefulness of multiattribute utility theory. A busy decision maker needs a multiattribute utility theory assessment technique which can be easily taught and used, though it may lack the theoretical elegance of techniques proposed by, for example, Keeney and Raiffa. In this paper, we present the interactive multiattribute utility function method which solicits additional information from the decision maker sequentially, through an interactive process, until a final choice is determined. The contribution of this method is in reducting the information burden on the decision maker and thus providing ractical assistance to him. Finally through numerical example, the multiattribute utility function method was compared with the interactive ε-constraint method which was developed by the author for solving the multiobjective optimization problems.

부산지역 중소제조업의 경쟁력 강화방안

이창효 세종 1996 삶의 질과 지역불균형 Vol.1996 No.1

공명 광음향셀의 설계

이창효 漢陽大學校 基礎科學硏究所 1990 基礎科學論文集 Vol.9 No.-

공명광음향셀에 대한 공명이론으로부터 계산하여 얻은 수치로 광음향신호의 세기가 최대로 되는 공명광음향셀의 크기를 결정하였다. 이 때 lock-in 증폭기의 측정감도, 마이크로폰의 감도와 직경 등을 고려하여 가능한 한 셀의 크기를 작게 하였다. 결정된 최적 공명광음향셀에서의 광음향신호의 증폭도는 약 19.4이었다. The size of the optimal resonance photoacoustic cell is determined from the numerical values obtained by use of the previous developed resonance theory, considering the measurement accuracy of the lock-in amplifier and the response an diameter of the condenser microphone. It is found that the amplification of the signals in the resonance photoacoustic cell turns out to be 19.4 for the determined values of the optimal size.

多次元尺度構成法의 多屬性意思決定問題에의 適用

李昌孝 新羅大學校 1991 論文集 Vol.31 No.1

The problems of multiple criteria decision making can be broadly classified into two categories: multiple attribute decision making and multiple objective decision making. In actual practice this classification is well fitted to the two facets of problem solving-multiple attibute decision making is for selection, multiple objective decision making is for design. In this paper. we are focused on the applicability of multidimensional scaling tomultiple attribute decision making problems. Multidimensional scaling is a set of mathematical techniques that enable a research to uncover the hidden structure of dta bases. When alternatives have too many attributes or in some cases have vague or unknown attributes, multidimensional scaling has great advantage in the solution of multiple attribute decision making problems. The decision maker's orderings of the proximities of pairs of alternatives can be used to construct a multidimensional spatial representation. Alternatives ae represented by points in this space and the points that are close together are assumed to be close together in terms of preference. The decision maker is asked to \locate his ideal alternative in this space. and then the distance from the ideal point is measured in order to rank the alternatives in terms of preference. However this multidimensional scaling procedure suggests several points. For one thing each pair of alternatives given in the judgement must be ranked in terms of its similarity. This requires quite fine discrimination. In addition, the configuration is not unique, and the distance measure used to form the configuration assumes that the attributes are independent. In spite of the greater burden, this method is particularly useful when the number of attributes is quite large, say more than 7. and when the majority of attributes are expressed in the qualitative way.

多目的下에서의 意思決定問題에 관한 諸技法의 硏究

李昌孝 新羅大學校 1983 論文集 Vol.14 No.-

The nature of major decision problems has changed drastically in recent years, and serious doubt has been raised as to the adequacy of traditional decision techniques. The awareness of a multiplicity of different objectives in decision-making and management has evoked the need for more adeguate techniques which take into account the multidimensionality and heterogeneity of individual, social or entrepreneurial behaviour. The need for such adjusted methods is even more apparent due to the mutually conflicting or noncommensurable nature of many objectives. Recently several attempts have been made to develop more adequate theories and methods which take into account explicitly the existence of multiple criteria in decision-making. In this paper the author presents various multiobjective decisionmaking techniques for solving multiobjective problems by categorizing them into the multiattribute utility (MAU) models and the goal programming(GP) method.

수도권 노후공동주택단지의 입지 특성 및 잠재력 분석

이창효,이삼수 한국감정평가학회 2019 감정평가학논집 Vol.18 No.3

The purpose of this study is to propose and apply analytical methodologies for the spatial distribution, location characteristics, and potential for urban regeneration of aged apartment complexes through constructing a spatial database of Seoul metropolitan apartment complexes. The detailed items of spatial distribution characteristics of apartment complex consisted of the number of complexes the number of households, and developed density, and those of location characteristics and potential included rail availability, highway and urban motorway availability, additional development capacity, and potential demands related to residential development. As a result of the analyses for these items, aged apartment complexes over 30 years after construction were relatively better in the regeneration conditions such as location characteristics and potential, compared to the apartment complexes for less than 15 years. 본 논문은 수도권에 소재한 공동주택단지의 공간 데이터베이스 구축을 위해 필요한 주요 항목을 설정하고, 관련 자료의 수집ㆍ가공을 통해 구축한 공동주택단지 공간 데이터베이 스를 활용하여 공간적 분포와 입지 특성 및 잠재력에 대한 분석 방법론을 제안하였다. 주 요 분석 내용과 결과는 다음과 같다. 첫째, 공동주택관리정보시스템의 단지정보를 활용한 공동주택단지 공간 데이터베이스 구축 과정과 방법론을 제시하였다. 둘째, 공동주택단지 의 분포 특성 분석 항목으로 단지 및 세대수 그리고 개발밀도(용적률)를 선정하였고, 입 지 및 잠재력 항목으로는 철도 이용성, 고속도로 및 도시고속도로 이용성, 개발가능용량, 주택 잠재수요 등을 선정하였다. 셋째, 공동주택단지의 입지 및 잠재력 항목의 구축 자료 는 노후공동주택단지 재생을 위한 사업성 검토에 활용할 수 있는 유용한 정보가 될 수 있다. 넷째, 분석 결과에서 주목할 만한 점은 양호한 입지와 추가적인 개발가능용량을 갖 는 경과년수 30년 초과 노후공동주택단지에 비해, 경과년수 15년 이하의 공동주택단지 의 입지 특성은 상대적으로 좋지 않은 뿐만 아니라 현재의 개발밀도 역시 높은 것으로 나타나, 향후 노후화에 따른 공동주택단지 재생에 있어 상대적으로 불리한 조건임을 확 인하였다.

多目的計劃法에서의 非劣位解 및 選好解 探索

李昌孝 新羅大學校 1985 論文集 Vol.18 No.-

The noninferior solutions searching methods(e, g., weighting method and e-constraint method) develop a representation of the noninferiour set for consideration by the decision maker (DM), and emphasize the development of information about a multiobjective problem that is presented to a DM in a manner that allows the range of choice and the trade-offs among objectives to be well understood. However, one disadvantage which has severely limited there method's practical applicability is that they usually generate a large number of noninferior solutions; it becomes almost impossible for the DM to choose one which is the most satisfactory, i, e. , a preferred solution among them. Therefore, the methods are generally incorporated into some of the preferred solution searching methods. Among the preferred solution searching methods two representative methods, namely, the multiattribute utility function (MAUF) method and the surrogate worth trade-off(SWT) method use global and local utility (preferrence) modelling, respective. However, the MAUF method developed by Keeney and Raiffa have critical disadvantage such that it may force the DM to fit a function not truly representing his or her preferences. On the other hand, the SWT method developed by Haimes and Hall based on local utility function modelling, provides an alternative approach that avoids the restrictive assumpt-ions. Instead of specifying the utility function globally, their procedures construct a sequence of local preference approximations of it. The SWT method uses the e-constraint problem as a means of generating noninferior solutions. Trade-offs among objectives, whose values are expressed by values of strictly positive Lagrangian multipliers, are used as a medium. The DM responds by expressing his degree of preference over the prescribed trade-offs and by assigning numerical values to each surrogate worth function. This method guarantees the generated solution in each iteration to be Parto optimal and the DM can select his preferred solution from among noninferior solutions. However, original version of the SWT method has many disadvantages. Therefore, in this paper, a new multiobjective decision making technique for solvlng multiobjective optimization problems: the Ready Surrogate Worth Trade-off(RSWT) method which belongs to revised SWT methods is presented by the author. Using this technique, the preferred solution for the DM can be described more conveniently and efficiently from among a Pareto optimal solution sat.