Information uncertainty evaluated by parameter estimation and its effect on reliability-based multiobjective optimization

Ito, Makoto; Kogiso, Nozomu

doi:10.1299/jamdsm.2016jamdsm0083

Cited by 4 publications

(7 citation statements)

References 20 publications

(14 reference statements)

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“…With this new notation, ( 13) can be equivalently rewritten as For U and U defined in Sect. 3.2, Theorem 4 demonstrates that (34) is equivalent to (25) and (26).…”

Section: Robustness Against Uncertainty In Distribution Typementioning

confidence: 93%

“…That is, we require the reliability constraint to be satisfied for any input distribution p satisfying p ∈ P . A main assertion of this section is that by simply setting instead of = − −1 ( ) , constraint ( 36) is equivalent to (25) and (26) in Theorem 4. We can show this fact in the following manner.…”

Section: Robustness Against Uncertainty In Distribution Typementioning

confidence: 97%

“…Proof It follows from Lemmas 1 and 2 that ( 14) and (15) in Theorem 3 can be equivalently rewritten as We use W = + to observe that this is reduced to which is straightforwardly equivalent to (25) and (26). ◻…”

Section: Theoremmentioning

confidence: 99%

“…It should be emphasized that Theorem 4 converts the set of infinitely many reliability constraints in (12) into two deterministic constraints, i.e., (25) and (26). The latter constraints can be handled within the framework of conventional (deterministic) optimization.…”

Section: Theoremmentioning

confidence: 99%

“…However, it is often difficult to obtain statistical information with sufficient accuracy in practice. This has incented recent intensive studies on RBDO with incomplete statistical information [12,13,19,25,26,28,29,43,47,48,57,61,62,64].…”

Section: Introductionmentioning

confidence: 99%

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Structural reliability under uncertainty in moments: distributionally-robust reliability-based design optimization

Kanno

2021

Japan J. Indust. Appl. Math.

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This study considers structural optimization under a reliability constraint, in which the input distribution is only partially known. Specifically, when it is only known that the expected value vector and the variance-covariance matrix of the input distribution belong to a given convex set, it is required that the failure probability of a structure should be no greater than a specified target value for any realization of the input distribution. We demonstrate that this distributionally-robust reliability constraint can be reduced equivalently to deterministic constraints. By using this reduction, we can handle a reliability-based design optimization problem under the distributionally-robust reliability constraint within the framework of deterministic optimization; in particular, nonlinear semidefinite programming. Two numerical examples are solved to demonstrate the relation between the optimal value and either the target reliability or the uncertainty magnitude.

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“…With this new notation, ( 13) can be equivalently rewritten as For U and U defined in Sect. 3.2, Theorem 4 demonstrates that (34) is equivalent to (25) and (26).…”

Section: Robustness Against Uncertainty In Distribution Typementioning

confidence: 93%

Section: Robustness Against Uncertainty In Distribution Typementioning

confidence: 97%

Section: Theoremmentioning

confidence: 99%

Section: Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Structural reliability under uncertainty in moments: distributionally-robust reliability-based design optimization

Kanno

2021

Japan J. Indust. Appl. Math.

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A data-driven approach to non-parametric reliability-based design optimization of structures with uncertain load

Kanno

2019

Struct Multidisc Optim

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Satisficing Strategy in Engineering Design

Guo,

Chen

2024

Journal of Mechanical Design

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In engineering-design problems, usually, there are multiple goals with different units, continuous and discrete variables, nonlinear equations, nonconvex equations, and coupled decisions. Ideally, all goals' target are reached simultaneously within the feasible space. However, the optimal solution may not be available. To deal with all those complexities, a modeling strategy named “satisficing” was proposed in the 1980s. The satisficing strategy allows designers to find “good enough” but may not be optimal solutions. In this paper, we review the publications applying the satisficing strategy on engineering-design problems, categorize the methods regarding the design stages they manage. We define the methods dealing with all four design stages – formulation, approximation, solution, and evaluation – as the whole process satisficing methods. We review the publications using the whole process satisficing strategy in great detail. In the past 30 years, the whole process satisficing strategy has been improved and applied to a wide variety of engineering-design problems, based on which derived methods, concepts, and platforms are developed. We generalize the specialties, advantages, and scope of applications of the methods in the whole process satisficing strategy. We expect this paper provides information on when and how designers may apply satisficing for their problems.

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Information uncertainty evaluated by parameter estimation and its effect on reliability-based multiobjective optimization

Cited by 4 publications

References 20 publications

Structural reliability under uncertainty in moments: distributionally-robust reliability-based design optimization

Structural reliability under uncertainty in moments: distributionally-robust reliability-based design optimization

A data-driven approach to non-parametric reliability-based design optimization of structures with uncertain load

Satisficing Strategy in Engineering Design

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