• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.2
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• pp.39-60
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• 1995

In this paper, an integration of stochastic dynamic programming (SDP), integer goal programming (IGP) and analytic hierarchy process (AHP) is proposed to handle multiobjective-multicriteria sequential decision making problems under uncertainty inherent in R & D investment planning. SDP has its capability to handle problems which are sequential and stochastic. In the SDP model, the probabilities of the funding levels in any time period are generated using a subjective model which employs functional relationships among interrelated parameters, scenarios of future budget availability and subjective inputs elicited from a group of decision makers. The SDP model primarily yields an optimal investment planning policy considering the possibility that actual funding received may be less than anticipated one and thus the projects being selected under the anticipated budget would be interrupted. IGP is used to handle the multiobjective issues such as tradoff between economic benefit and technology accumulation level. Other managerial concerns related to the determination of the optimal project portifolio within each stage of the SDP model. including project selection, project scheduling and annual budget allocation are also determined by the IGP. AHP is proposed for generating scenario-based transformation probabilities under budgetary uncertainty and for quantifying the environmental risk to be considered.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.1 s.28
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• pp.3-10
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• 2004

These days GIS have rapidly deployed as input and solutions to marketing decision making problems and corresponding decision support systems in many countries including Korea. Its powerful spatial analysis tools along with data integration and graphic display capabilities let many retailers and manufacturers in fashion industry to accept GIS as a useful mean for their decision making systems. At this moment, this paper presents many facets of discussions on how GIS be applied to fashion marketing decision making problems. From provoking several questions on current fashion marketing decision making system to explaining multiattribute decision making and multiobjective decision making as tools for decision making analysis and discussing some implementation issues, this paper revealed many aspects of GIS and fashion marketing decision support system from integration point of view.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.310-320
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• 1999

A mixed model assembly line (MMAL) is a special type of production line where a variety of product models similar in product characteristics are assembled. Determining the model sequence is an important problem for the efficient use of MMALs. This paper considers interactive multiobjective decision making problems for MMAL sequencing. Evolution program is employed as an underlying framework. In this study, a way of approximating the linear utility function is first studied. To improve its search efficiency to the solution space preferred by a decision maker, some modifications of a standard evolution program are made: operating several subpopulations instead of a single population and merging two or more subpopulations to a single subpopulation, and using a Pareto pool. Extensive computational experiments are carried out to verify the performance of the proposed approach. The computational results show that our approach is promising in solution quality.

• Journal of Environmental Impact Assessment
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• v.5 no.1
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• pp.47-60
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• 1996

Nitrate contamination problems from groundwater supplies have been reported throughout many countries in the world, including Korea. Nitrate salts can induce methemoglobinemia and possibly human gastric cancer. To reduce human health risk from nitrate in groundwater supplies, several nitrate risk-management strategies can be developed based on the acceptable level of human health risk, the reasonableness of nitrate-control cost, and the technical feasibility of nitrate-control methods. However, due to a lack of available information, assessing risk, cost and technical feasibility contains elements of uncertainty. In the present paper, a nitrate risk-management methodology using fuzzy sets in combination with a multiobjective decision-making (MODM) technique is developed to assist decision makers in evaluating, with uncertain information, various nitrate risk-management strategies in order to decide a proper strategy.

• Structural Engineering and Mechanics
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• v.71 no.2
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• pp.139-151
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• 2019

In a project schedule, it is possible to reduce the time required to complete a project by allocating extra resources for critical activities. However, accelerating a project causes additional expense. This issue is addressed by finding optimal set of time-cost alternatives and is known as the time-cost trade-off problem in the literature. The aim of this study is to identify the optimal set of time-cost alternatives using a multiobjective teaching-learning-based optimization (TLBO) algorithm integrated with the non-dominated sorting concept and is applied to successfully optimize the projects ranging from a small to medium large projects. Numerical simulations indicate that the utilized model searches and identifies optimal / near optimal trade-offs between project time and cost in construction engineering and management. Therefore, it is concluded that the developed TLBO-based multiobjective approach offers satisfactorily solutions for time-cost trade-off optimization problems.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.589-605
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• 1996

Recycling of used products and components has been considered as one of promising strategies for resolving environmental problems. In this respect, most manufacturing companies begin to consider possible recycling (e.q., reuse or re-production) of the components contained in their products. The primary objective of this research is to develop a multiple criteria decision making model for systematic management of recycle-oriented manufacturing components. The production planning problem of recycle-oriented manufacturing components is first formulated as a multiobjective mixed 0-1 integer programming model with three conflicting objectives. An interactive multiple criteria decision making method is then developed for solving the mathematical model. Also, an Input/Output analysis software is developed to help practitioners apply the model to real problems without much knowledge on computers and mathematical programming. A numerical example is used in examining the validity of the proposed model and to investigate the impact of the input variables on recycling production strategy.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.137-140
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• 1998

In this study, we have developed algorithms to find more effective solutions for compensatory decision-making problems in the case of the decision maker with fuzziness which can occur in a real world fuzzy environment. We have applied the algorithm to the problems related to the structural reform of the capital and the number of workers in the local industry. We have selected Taegu city for this study. In this study, we have determined the capital and the number of workers, satisfying maximum productivity and minimum air and water pollution under the constraints such as capital-labor ratio, the demand for land and water and the fluctuation of the capital and the number of workers. The determined capital and the number of workers could improve the competitive advantage of Taegu city and could be utilized as criteria for the compilation of the budget, determination of policy for supporting plan of companies, the forecast of number of workers and the training plan of workers.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1296-1301
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy -neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.421-426
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.303-308
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.