• Journal of the Korea Society of Computer and Information
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• v.8 no.1
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• pp.156-165
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• 2003

The purpose of this study is concerned with possibilistic decision making model (PDMM) that can be used to help CEO and information systems managers decide which information systems should be selected. The application of IT which has influence on rapidly changed environment of enterprise plays an important role in enterprise's activity. When enterprise outsource IT, it is very important to select vendors that reflect goals and constraints of organization. For this purpose, mathematical model in which possibilistic programming is applied is suggested in this study. Although many researches have conducted in conventional programming and stochastic programming. they are still limited in solving practical problems and imprecise/uncertain situations. Multiple decision making model in which impreciseness of input variable is considered can be constructed in PDMM.

• Journal of the Korea Society of Computer and Information
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• v.9 no.4 s.32
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• pp.203-214
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• 2004

This paper presents a practical application of possibilistic programming in a information system projects planning model. The estimate of the parameters of the model is often a problematic task for the decision maker(DM), Who has imprecise information or express his considerations subjectively. In this case. possibilistic decision making models can provide an important aspect in handling practical decision making problems. We suggest CPM(Critical Path Method) applying possibilistic programming. CPM is an approach to planning and coordinating large projects by directing managerial focus to the project's most critical aspects and providing completion time of project and beginning time of each activity. This model is an aid in the control of considering aspiration levels by the DM, the fuzziness of decision making, and computational efficiency. The problem is solved by using GINO computer package and the best compromised solution is found.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.506-511
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• 1998

In this paper we consider multiobjective linear programming problems with coefficients of the objective functions specified by possibility distributions. Possibly and necessarily efficient solution sets are defined as funny solution sets whose membership grades represent possibility and necessity degrees to which a feasible solution is efficient. Considering efficiency condition and its dual condition in ordinary multiobjective linear programming problem, we propose efficiency test methods based on an extreme ray generation method. Since the proposed methods can be put in the part of a bi-section method, we can develop calculation and methods of the degree of possible and necessary efficiency for feasible solutions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.2
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• pp.51-60
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• 2008

A solution method for fuzzy linear programs is proposed. A fuzzy linear program is converted to a crisp linear program with average indices being applied to the objective function and constraints. A comparative analysis between the proposed average index approach and the possibilistic approach is given. As an application example, the proposed method is applied to the linear programming model for fuzzy data envelopment analysis, and the result is compared with that of the possibilistic approach.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.67-72
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity models have been recently utilized, which are based on quadratic programming approach giving more diverse spread coefficients than a linear programming one. SVM also uses quadratic programming approach whose another advantage in interval regression analysis is to be able to integrate both the property of central tendency in least squares and the possibilistic property In fuzzy regression. However this is not a computationally expensive way. SVM allows us to perform interval nonlinear regression analysis by constructing an interval linear regression function in a high dimensional feature space. In particular, SVM is a very attractive approach to model nonlinear interval data. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function for interval nonlinear regression model with crisp inputs and interval output. Experimental results are then presented which indicate the performance of this algorithm.

• Journal of applied mathematics & informatics
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• v.42 no.4
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• pp.831-854
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• 2024

The transportation problem (TP) is one of the earliest and the most significant implementations of linear programming problem (LPP). It is a specific type of LPP that mostly works with logistics and it is connected to day-to-day activities in our everyday lives. Nowadays decision makers (DM's) aim to reduce the transporting expenses and simultaneously aim to reduce the transporting time of the distribution system so the bi-objective transportation problem (BOTP) is established in the research. In real life, the transportation parameters are naturally uncertain due to insufficient data, poor judgement and circumstances in the environment, etc. In view of this, neutrosophic bi-objective transportation problem (NBOTP) is introduced in this paper. By introducing single-valued trapezoidal neutrosophic numbers (SVTrNNs) to the co-efficient of the objective function, supply and demand constraints, the problem is formulated. The DM's aim is to determine the optimal compromise solution for NBOTP. The extended weighted possibility mean for single-valued trapezoidal neutrosophic numbers based on [40] is proposed to transform the single-valued trapezoidal neutrosophic BOTP (SVTrNBOTP) into its deterministic BOTP. The transformed deterministic BOTP is then solved using the dripping method [10]. Numerical examples are provided to illustrate the applicability, effectiveness and usefulness of the solution approach. A sensitivity analysis (SA) determines the sensitivity ranges for the objective functions of deterministic BOTP. Finally, the obtained optimal compromise solution from the proposed approach provides a better result as compared to the existing approaches and conclusions are discussed for future research.