Multiobjective linguistic optimization

Carlsson, Christer; Fuller, Robert C.

doi:10.1016/s0165-0114(99)00020-2

Cited by 40 publications

(21 citation statements)

References 13 publications

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“…Indeed, human judgments are vague or fuzzy in nature and thus it may not be appropriate to represent them by precise numerical values. A more realistic approach could be to use linguistic variables to model human judgments (Carlsson and Fuller 2000). In this paper, a new fuzzy closeness methodology for solving MADM problems under fuzzy environments is proposed.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy closeness approach to fuzzy multi-attribute decision making

2007

Fuzzy Optim Decis Making

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The aim of this paper is to develop a new fuzzy closeness (FC) methodology for multi-attribute decision making (MADM) in fuzzy environments, which is an important research field in decision science and operations research. The TOPSIS method based on an aggregating function representing "closeness to the ideal solution" is one of the well-known MADM methods. However, while the highest ranked alternative by the TOPSIS method is the best in terms of its ranking index, this does not mean that it is always the closest to the ideal solution. Furthermore, the TOPSIS method presumes crisp data while fuzziness is inherent in decision data and decision making processes, so that fuzzy ratings using linguistic variables are better suited for assessing decision alternatives. In this paper, a new FC method for MADM under fuzzy environments is developed by introducing a multi-attribute ranking index based on the particular measure of closeness to the ideal solution, which is developed from the fuzzy weighted Minkowski distance used as an aggregating function in a compromise programming method. The FC method of compromise ranking determines a compromise solution, providing a maximum "group utility" for the "majority" and a minimum individual regret for the "opponent". A real example of a personnel selection problem is examined to demonstrate the implementation process of the method proposed in this paper.

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Section: Introductionmentioning

confidence: 99%

A fuzzy closeness approach to fuzzy multi-attribute decision making

2007

Fuzzy Optim Decis Making

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“…the replacement of probabilistic distributions by possibilistic ones) does not reduce the bullwhip effect [2]. We proved, however that by including better and better estimates of future sales during period one, D 1 , the variance of z 1 can be essentially reduced by replacing the old rule for ordering with an adjusted fuzzy rule [3].…”

Section: The Bullwhip Effect Causes Some Problems For Damentioning

confidence: 77%

“…[2,3]) and will use this rather cumbersome phenomenon as an illustration of complex decision problems and as a background for a critical discussion of the DA paradigm.…”

Section: The Bullwhip Effect Causes Some Problems For Damentioning

confidence: 99%

“…In other words, there is no hope to avoid the bullwhip effect by changing the ordering policy, as it is difficult to motivate people to act in an irrational way. Since one of the key drivers of the bullwhip effect seems to be the (precise) optimal strategy, we decided to increase flexibility by introducing fuzzy numbers into the bullwhip models [2] and used the following equation for optimal order quantity with fuzzy numbers…”

Section: The Bullwhip Effect Causes Some Problems For Damentioning

confidence: 99%

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A position paper on the agenda for soft decision analysis

Carlsson

Fuller

2002

Fuzzy Sets and Systems

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In this position paper we shall undertake a critical scrutiny of the decision analysis (DA) paradigm on the basis of experience we gained from working on the bullwhip effect. We had some success with sorting out the complexities of the bullwhip by using fuzzy numbers in the bullwhip models, and we found out that this points the way to a more viable approach to sort out complex decision problems. As a positive criticism of the DA paradigm we have collected the insights we have gained in an agenda for soft decision analysis (SDA), which we hasten to add is not complete but -hopefully -an opening for focused research and continuing development. Keywords: Decision analysis; Soft decision analysis Decision analysisThe paradigm of DA aims at both a descriptive and a prescriptive treatment of decision-making, i.e. both to describe how people make decisions and to prescribe how they should make decisions in order to maximize their utility. Keeney and Raiffa (1976, [4]) summarize their paradigm in a five-step process (pp 5-7; here partly abbreviated):1. Pre-analysis. We assume that there is a unitary decision maker who is undecided about the course of action he or she should take in a particular problem. The problem has been identified and the viable action alternatives are given.

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“…Nevertheless, we want to keep the knowledge-rich substance of the models and methods as we progress to more MP-like modelling. This has long been considered a methodological contradiction, but it has become apparent in recent years [5] that this standard objection is not necessarily true. A combination of linguistic variables and fuzzy logic is emerging as a good approach to have knowledge-rich imprecision, a systematic and firm methodological structure, and effective and fast analytical MP-algorithms.…”

Section: Introductionmentioning

confidence: 99%

Optimization with linguistic variables

Carlsson

Fuller

2003

Fuzzy Sets Based Heuristics for Optimization

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Abstract. We consider fuzzy mathematical programming problems (FMP) in which the functional relationship between the decision variables and the objective function is not completely known. Our knowledge-base is supposed to consists of a block of fuzzy if-then rules, where the antecedent part of the rules contains some linguistic values of the decision variables, and the consequence part is either a linguistic value of the objective function or a linear combination of the crisp values of the decision variables. In this paper we suggest the use of an adequate fuzzy reasoning method to determine the crisp functional relationship between the objective function and the decision variables, and to solve the resulting (usually nonlinear) programming problem to find a fair optimal solution to the original fuzzy problem. Furthermore, we illustrate how the optimal solution may change if we are able to refine the rule base by introducing some non-monotonicity (dependency) rules.

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Multiobjective linguistic optimization

Cited by 40 publications

References 13 publications

A fuzzy closeness approach to fuzzy multi-attribute decision making

A fuzzy closeness approach to fuzzy multi-attribute decision making

A position paper on the agenda for soft decision analysis

Optimization with linguistic variables

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