Can MS/OR Sell Itself Well Enough?

Tingley, G A

doi:10.1287/inte.17.4.41

Cited by 24 publications

(11 citation statements)

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“…In many real cases dealing with certain phenomena such as; fuzziness and randomness simultaneously with the optimization problem as; fuzzy stochastic linear programming problems under uncertainty, is more inappropriate to get a compromised solution to such optimization model [22,28,40].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution

Hamadameen

Zainuddin

2014

AIP Conference Proceedings

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This study deals with multiobjective fuzzy stochastic linear programming problems with uncertainty probability distribution which are defined as fuzzy assertions by ambiguous experts. The problem formulation has been presented and the two solutions strategies are; the fuzzy transformation via ranking function and the stochastic transformation when α− cut technique and linguistic hedges are used in the uncertainty probability distribution. The development of Sen's method is employed to find a compromise solution, supported by illustrative numerical example.

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Section: Introductionmentioning

confidence: 99%

Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution

Hamadameen

Zainuddin

2014

AIP Conference Proceedings

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“…Therefore, APP is designed to deal with productive efforts as a whole, not in terms of detailed plans. A study of the literature on APP modeling concludes that linear programming (LP) is the most used approach for formulating the APP problem [2]. However, in practice, LP models assume that all required data input are deterministic and available.…”

Section: Introductionmentioning

confidence: 99%

Investigating the benefits of fuzzy mathematical programming approach for solving aggregate production planning

Omar

Jusoh

Omar

2012

2012 IEEE International Conference on Fuzzy Systems

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This paper introduces a fuzzy mixed-integer linear programming (FMILP) model to solve the aggregate production planning (APP) problem. The FMILP formulation is developed in which both fuzzy and possibilistic uncertainties appear in the same model. The main objective of this paper is to investigate the benefits of adopting fuzzy mathematical programming approach to model APP problems. To achieve the objective of this paper, a real industrial data from a resin manufacturing plant (see Omar and Teo [1]) were used to develop the proposed FMILP. In addition, the results of the FMILP were compared with the results from the deterministic model proposed by Omar and Teo [1]. The findings indicate that significant cost savings were achieved by adopting the fuzzy programming approach.

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“…(Kivija¨rvi, Korhonen, and Wallenius (1986), Lilien (1987), Tingley (1987), Meyer zu Selhausen (1989)). Nevertheless a strong discrepancy between the application in literature and the practical use can be acknowledged.…”

Section: Introductionmentioning

confidence: 99%

The Advantages of Fuzzy Optimization Models in Practical Use

Rommelfanger

2004

Fuzzy Optimization and Decision Making

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Classical mathematical programming models require well-defined coefficients and right hand sides. In order to avoid a non satisfying modeling usually a broad information gathering and processing is necessary. In case of real problems some model parameters can be only roughly estimated. While in case of classical models the vague data is replaced by ''average data'', fuzzy models offer the opportunity to model subjective imaginations of the decision maker as precisely as a decision maker will be able to describe it. Thus the risk of applying a wrong model of the reality and selecting solutions which do not reflect the real problem can be clearly reduced. The modeling of real problems by means of deterministic and stochastic models requires extensive information processing. On the other hand we know that an optimum solution is finally defined only by few restrictions. Especially in case of larger systems we notice afterwards that most of the information is useless. The dilemma of data processing is due to the fact that first we have to calculate the solution in order to define, whether the information must be well-defined or whether vague data may be sufficient. Based on multicriteria programming problems it should be demonstrated that the dilemma of data processing in case of real programming problems can be handled adequately by modeling them as fuzzy system combined with an interactive problem-solving. Describing the real problem by means of a fuzzy system first of all only the available information or such information which can be achieved easily will be considered. Then we try to develop an optimum solution. With reference to the cost-benefit relation further information can be gathered in order to describe the solution more precisely. Furthermore it should be pointed out that some interactive fuzzy solution algorithms, e.g. FULPAL provide the opportunity to solve mixed integer multicriteria programming models as well.

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Can MS/OR Sell Itself Well Enough?

Cited by 24 publications

References 0 publications

Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution

Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution

Investigating the benefits of fuzzy mathematical programming approach for solving aggregate production planning

The Advantages of Fuzzy Optimization Models in Practical Use

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