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Optimization Under Decision-dependent Uncertainty

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In engineering design or decision making problems, a large number of feasible solutions are available and to choose the solution which is the best one from this set, we need to concentrate on the uncertainty associated with the variables that lead to the optimal solution.

Probabilistic concepts can take care of the randomness that arises due to natural fluctuation or natural variations, but the uncertainty that comes into existence due to qualitative statements, vague statements, vague nature of the objective, and linguistic statements showing the willingness of the decision maker (like the solution is acceptable, low, satisfactory, etc.) cannot be addressed through probabilistic concepts, so we introduce the concept of fuzzy logic in solving the optimization problems. In the crisp definition of optimization problems, we have fresh conditions where arrangements abusing the imperatives or not fulfilling the target work are totally unsuitable, but rather, in fluffy improvement, the idea of degree is presented.

The solution becomes a matter of degree; that is, degree of acceptability or degree of satisfaction is associated with the constraints and the objective functions, and this way we provide latitude to the acceptability of a solution. This level of adequacy connected with the target capacities and limitations can be reflected through fluffy participation capacities. To deal with the situations where several stakeholders vaguely state their preferences as constraints or objective functions using linguistic statements, we convert these statements into fuzzy sets or fuzzy membership functions and then using some technique find out the best “compromise solution.”

In fuzzy optimization, we do not distinguish between the objective functions and the constraints; instead we refer to them as fuzzy goals, represented in the form of fuzzy sets defined by their respective membership functions. So, the latitude or uncertainty present in the decision making is tackled through these membership functions. In addition to the fuzzy goals, we can likewise have fresh limitations displaying the physical conditions or innovative practicality that must be met in a specific arrangement. The entire thought of fluffy streamlining is to consider scope in the imperatives and adaptability in the goal work. Rather than a 0-1 write arrangement, we take into account some infringement of the first imperatives to some degree, set certain breaking point for the goal work, and acknowledge arrangements on the two sides of the cutoff to various degrees.

The target work and the arrangement of limitations are changed over into fluffy sets; their related enrollment capacities are characterized and after that all the participation capacities are consolidated to decide the fluffy choice. Through fluffy advancement, the inclinations of the leader are measured and the vulnerability because of dubiousness, imprecision, et cetera, which is basic in basic leadership issues, is handled utilizing enrollment capacities.

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Optimization Under Decision-dependent Uncertainty. (2019, Jun 12). GradesFixer. Retrieved June 29, 2022, from
“Optimization Under Decision-dependent Uncertainty.” GradesFixer, 12 Jun. 2019,
Optimization Under Decision-dependent Uncertainty. [online]. Available at: <> [Accessed 29 Jun. 2022].
Optimization Under Decision-dependent Uncertainty [Internet]. GradesFixer. 2019 Jun 12 [cited 2022 Jun 29]. Available from:
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