• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.82-93
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• 2005

Facility layout problems (FLP) are usually treated as design problems. Lack of systematic and objective tools to compare design alternatives results in decision-making to be dominated by the experiences or preferences of designers or managers. To increase objectivity and effectiveness of decision-making in facility layout selections, a decision support model is necessary. We proposed a decision model, which regards the FLP as a multi-attribute decision making (MADM) problem. We identify sets of attributes crucial to layout selections, quantitative indices for attributes, and methods of ranking alternatives. For a requested facility layout design, many alternatives could be developed. The enormous alternatives, various attributes, and comparison of assigned qualitative values to each attribute, form a complicated decision problem. To treat facility layout selection problems as a MADM problem, we used the linear assignment method to rank before selecting those high ranks as candidates. We modelled the application of the Nemawashi process to simulate the group decision-making procedure and help efficiently achieve agreement. The electronics manufacturing service (EMS) industry has frequent and costly facility layout modifications. Our models are helpful to them. We use an electronics manufacturing service company to illustrate the decision-making process of our models.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.15-40
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• 1994

We designed a knowledge-based decision support system for structuring semi-or unstructured problems. Problem structuring involves extraction of the relevant factors from the identified problem, and model construction that represents the relationships among those factors. In this research, we employed a directed graph called Influence Deiagram as a tool for problem structuring. In particular, our proposed system is designed as a shell. Therefore, a decision maker can change the content of the knowledge base to suit his/her own interested domain.

• IE interfaces
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• v.13 no.3
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• pp.539-547
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• 2000

An architecture of a Web-based Decision Support system for mathematical analysis is presented. Front-end modules provide web-client GUI environment for mathematical analysis. The networking architecture is built upon client/server system by Java socket and accesses database by JDBC in WWW. Back-end modules provide decision supporting service and data management for mathematical programming analysis. In the back-end any analysis tools, such as mathematical optimizer, simulation package, or statistics package can be used. As an application example for this implementation, optimal facility replacement decision problem is selected. In the implementation the optimal facility replacement decision problem is formulated as a shortest path problem. It uses Oracle DB and CPLEX package as the mathematical optimizer. While ORAWeb is designed and implemented on the optimal facility replacement problem, it can easily be extended to any decision supporting problems that would require mathematical optimization process.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.33-49
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• 2002

CBM (Condition-Based Maintenance) has increasingly drawn attention in industry because of its many benefits. CBM Problem Is characterized as a state-dependent scheduling model that demands simultaneous maintenance actions, each for an attribute that influences on machine condition. This problem is very hard to solve within conventional Markov decision process framework. In this paper, we present an intelligent machine maintenance scheduler, for which a new incremental decision tree learning method as evolutionary system identification model and shortest path problem as schedule generation model are developed. Although our approach does not guarantee an optimal scheduling policy in mathematical viewpoint, we verified through simulation based experiment that the intelligent scheduler is capable of providing good scheduling policy that can be used in practice.

• Journal of the Korean Operations Research and Management Science Society
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• v.10 no.1
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• pp.41-53
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• 1985

The influence diagram is a new conceptual tool that can be used for structuring a strategic decision problem in decision analysis. It has a graphical representation of probabilistic dependence among variables in the decision problem. In this formal procedures for constructuring the influence diagram and for translating it into the corresponding decision tree are studied. An example that shows the power of the infuence diagram is shown.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data (a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology fur sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.281-287
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• 1999

Decision class analysis (DCA) is viewed as a classification problem where a set of input data (situation-specific knowledge) and output data(a topological leveled influence diagram (ID)) is given. Situation-specific knowledge is usually given from a decision maker (DM) with the help of domain expert(s). But it is not easy for the DM to know the situation-specific knowledge of decision problem exactly. This paper presents a methodology for sensitivity analysis of DCA under incomplete information. The purpose of sensitivity analysis in DCA is to identify the effects of incomplete situation-specific frames whose uncertainty affects the importance of each variable in the resulting model. For such a purpose, our suggested methodology consists of two procedures: generative procedure and adaptive procedure. An interactive procedure is also suggested based the sensitivity analysis to build a well-formed ID. These procedures are formally explained and illustrated with a raw material purchasing problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.13-19
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• 1992

MODM(multiobjective decision-making)problem is very complex system for the analysist and decision maker. Therefore, it requires suitable MODM method to solve multiobjective decision-making problem. This paper presents an interactive fuzzy decision making method for solving multiobjective nonlinear programming problems with fuzzy goals and $\alpha$-cut set of fuzzy numbers. In our interactive method, if the decision maker specifies the degree $\alpha$of the objective value and the imprecise goals, λ-mux problem is solved. To examplify the proposed method, an interactive computer programming written in FORTRAN and an illustrate numerical example along with computer outputs are presented.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1187-1192
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• 2008

In this paper, a versatile multi-objective optimization concept for fatigue life prediction is introduced. Multi-objective decision making in engineering design refers to obtaining a preferred optimal solution in the context of conflicting design objectives. Compromise decision support problems are used to model engineering decisions involving multiple trade-offs. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. This paper gives an interpretation of the decision parameters as governing both the relative importance of the attributes and the degree of compensation between them. The approach utilizes a response surface model, the compromise decision support problem, which is a multi-objective formulation based on goal programming. Examples illustrate the concepts and demonstrate their applicability.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.2
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• pp.131-144
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• 1992

Even though nonhomogeneous Markov Decision Processes subsume homogeneous Markov Decision Processes and are more practical in the real world, there are many results for them. In this paper we address the nonhomogeneous Markov Decision Process with objective to maximize average reward. By extending works of Ross [17] in the homogeneous case adopting the result of Bean and Smith [3] for the dicounted deterministic problem, we first transform the original problem into the discounted nonhomogeneous Markov Decision Process. Then, secondly, we transform into the discounted deterministic problem. This approach not only shows the interrelationships between various problems but also attacks the solution method of the undiscounted nohomogeneous Markov Decision Process.