• Journal of the Korea Society for Simulation
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• v.14 no.3
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• pp.43-54
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• 2005

For the effective design of automated distribution center composed of Automated Storage/Retrieval System, Automated Guided Vehicle System, and Conveyor System, we proposed an analysis method to determining. design and control parameters with multiple performance objectives using Compromise Programming, which can resolve the dilemma of conflicting objectives. The Evolution Strategy generates the optimal solutions for each objectives. The Analytic Hierarchy Process selects the best solution among the alternatives generated from Evolution Strategy. The Regression Analysis formulates the objective functions for each objectives. By reducing deviations between goal values and target values generated from Analytic Hierarchy Process, Compromise Programming determines design and control parameters by compromising the multiple objectives formulated using Regression Analysis. When the parameters of system are changed, this proposed analysis method has a benefit of reducing costs and time without repeating whole simulation run.

• Journal of applied mathematics & informatics
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• v.24 no.1_2
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• pp.449-460
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• 2007

Several fuzzy approaches can be considered for solving multi-objective transportation problem. This paper presents a fuzzy goal programming approach to determine an optimal compromise solution for the multiobjective transportation problem. We assume that each objective function has a fuzzy goal. Also we assign a special type of nonlinear (hyperbolic) membership function to each objective function to describe each fuzzy goal. The approach focuses on minimizing the negative deviation variables from 1 to obtain a compromise solution of the multiobjective transportation problem. We show that the proposed method and the fuzzy programming method are equivalent. In addition, the proposed approach can be applied to solve other multiobjective mathematical programming problems. A numerical example is given to illustrate the efficiency of the proposed approach.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.97-112
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• 1991

This paper describes two contrasting approaches to group decision making involving multiple criteria. A compromise programming method and the analytic hierarchy process are analyzed and compared by using an illustrative example of a computer model selection problem to demonstrate their usefulness as a viable tool for group decision making. This paper further considers some extensions and modifications of there two methods for future study.

• Water for future
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• v.57 no.6
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• pp.53-61
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• 2024

• Journal of Korea Water Resources Association
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• v.44 no.12
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• pp.1015-1029
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• 2011

Conventional MCDA techniques have been used in the field of water resources in the past. A GIS can offer an effective spatial data-handling tool that can enhance water resources modeling through interfaces with sophisticated models. However, GIS systems have a limited capability as far as the analysis of the value structure is concerned. The MCDA techniques provide the tools for aggregating the geographical data and the decision maker's preferences into a one-dimensional value for analyzing alternative decisions. In other words, the MCDA allows multiple criteria to be used in deciding upon the best alternatives. The combination of GIS and MCDA capabilities is of critical importance in spatial multi-criteria analysis. The advantage of having spatial data is that it allows the consideration of the unique characteristics at every point. The purpose of this study is to identify, review, and evaluate the performance of a number of conventional MCDA techniques for integration with GIS. Even though there are a number of techniques which have been applied in many fields, this study will only consider the techniques that have been applied in floodplain decision-making problems. Two different methods for multi-criteria evaluation were selected to be integrated with GIS. These two algorithms are Compromise Programming (CP), Spatial Compromise Programming (SCP). The target region for a demonstration application of the methodology was the Suyoung River Basin in Korea.

• Journal of Korean Society of Forest Science
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• v.83 no.3
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• pp.271-279
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• 1994

This paper descbibes a fuzzy multiobjective linear programming, which is a relatively new approach in forestry in solving forest management problems. At first, the fuzzy set theory is explained briefly and the fuzzy linear programming(FLP) and the fuzzy multiobjective linear programming(FMLP) are introduced conceptionally. With the information obtained from the study area in Thailand, a standard linear programming problem is formulated, and optimal solutions (present net worth) are calculated for four groups of timber price by this LP model, respectively. This LP model is reformulated to a fuzzy multiobjective linear programming model to accommodate uncertain timber values and with this FMLP model a compromise solution is attained. Optimal solutions of four objective functions for four timber price groups and the compromise solution are compared and discussed.

• 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 Korean Institute of Industrial Engineers
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• v.32 no.1
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• pp.42-50
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• 2006

The multi-criteria group decision making (MCGDM) problem is to determine the best compromise solution in a set of competing alternatives that are evaluated under conflicting criteria by decision maker (DM)s. In this paper, we propose a mixed-integer programming (MIP) model to solve MCGDM. The existing method based on minimizing a distance measure such as Median Approach can not guarantee the best compromise solution because the element of median point vector is defined with respect to each criteria separately. However, by considering all criteria simultaneously, we generate median point that is better for locating the best compromise solution. We also utilize the concept of spatial dispersion index (SDI) to produce a threshold value, which is used as a guideline to choose either the Utopian Approach or the Median Approach. And we suggest using CBITP (Convex hull of individual maxima Based Interactive Tchebycheff Procedure) to provide DMs with various Pareto-optimal solutions so that DMs have broad range of selection.

• Journal of Korean Society for Quality Management
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• v.25 no.4
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• pp.168-184
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• 1997

The primary objective of this paper is to develop an interactive computer graphic aided decision su, pp.rt system for the design of inspection plans for complex production systems. This research first develops a computer simulator, called Simulator for Inspection Systems(SIS), in order to represent the production system as a network model. The simulator elicits fundamental information about the system from the decision maker(DM) in an interactive manner. A decision su, pp.rt system is then proposed by combining SIS and Compromise Programming. The system simulates a set of possible inspection strategies implicitly, observes all the important measurements about the inspection system, and employs the compromise programming a, pp.oach to help the DM determine the best compromising allocation of inspection effort considering his/her preferences. Finally, the performance of the proposed system is evaluated using two test cases. The pros and cons of the system are also discussed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.659-662
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• 1998

In this paper, lower and upper possibility distributions are identified to reflect two extreme opinions in portfolio selection problems based on upper and lower possibility distributions are formalized as quadratic programming problems. Portfolios for compromising two extreme opinions from upper and lower possibility distributions and balancing the opinions of a group of experts can be obtained by quadratic optimization problems, respectively.