Journal ArticleDOI
Exploration of the effectiveness of physical programming in robust design
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TLDR
This paper synergistically integrate methods that had previously and independently been developed by the authors, thereby leading to optimal-robust-designs, and establishes the general superiority of physical programming over other conventional methods in solving multiobjective optimization problems.Abstract:
Computational optimization for design is effective only to the extent that the aggregate objective function adequately captures designer's preference. Physical programming is an optimization method that captures the designer's physical understanding of the desired design outcome in forming the aggregate objective function. Furthermore, to be useful, a resulting optimal design must be sufficiently robust/insensitive to known and unknown variations that to different degrees affect the design's performance. This paper explores the effectiveness of the physical programming approach in explicitly addressing the issue of design robustness. Specifically, we synergistically integrate methods that had previously and independently been developed by the authors, thereby leading to optimal-robust-designs. We show how the physical programming method can be used to effectively exploit designer preference in making tradeoffs between the mean and variation of performance, by solving a bi-objective robust design problem. The work documented in this paper establishes the general superiority of physical programming over other conventional methods (e.g., weighted sum) in solving multiobjective optimization problems. It also illustrates that the physical programming method is among the most effective multicriteria mathematical programming techniques for the generation of Pareto solutions that belong to both convex and non-convex efficient frontiers.read more
Citations
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Journal ArticleDOI
Survey of multi-objective optimization methods for engineering
R.T. Marler,Jasbir S. Arora +1 more
TL;DR: A survey of current continuous nonlinear multi-objective optimization concepts and methods finds that no single approach is superior and depends on the type of information provided in the problem, the user's preferences, the solution requirements, and the availability of software.
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Optimization Concepts and Applications in Engineering
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Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles
TL;DR: A comprehensive review of Uncertainty-Based Multidisciplinary Design Optimization (UMDO) theory and the state of the art in UMDO methods for aerospace vehicles is presented.
Journal ArticleDOI
A review of robust optimal design and its application in dynamics
TL;DR: The robust design of a vibration absorber with mass and stiffness uncertainty in the main system is used to demonstrate the robust design approach in dynamics as discussed by the authors, and the results show a significant improvement in performance compared with the conventional solution.
Journal ArticleDOI
Toward a Product System Modularity Construct: Literature Review and Reconceptualization
TL;DR: The paper constitutively defines product modularity in terms of component separability and component combinability, and an indirect operational definition is then proposed by operationalizing component separable and component Combinability.
References
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Theory of Games and Economic Behavior
TL;DR: Theory of games and economic behavior as mentioned in this paper is the classic work upon which modern-day game theory is based, and it has been widely used to analyze a host of real-world phenomena from arms races to optimal policy choices of presidential candidates, from vaccination policy to major league baseball salary negotiations.
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Decisions with Multiple Objectives: Preferences and Value Trade-Offs
TL;DR: In this article, a confused decision maker, who wishes to make a reasonable and responsible choice among alternatives, can systematically probe his true feelings in order to make those critically important, vexing trade-offs between incommensurable objectives.
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Multiple Criteria Optimization: Theory, Computation, and Application
TL;DR: Mathematical Background Topics from Linear Algebra Single Objective Linear Programming Determining all Alternative Optima Comments about Objective Row Parametric Programming Utility Functions, Nondominated Criterion Vectors and Efficient Points Point Estimate Weighted-sums Approach.
Journal ArticleDOI
A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems
Indraneel Das,John E. Dennis +1 more
TL;DR: In this article, the authors provide a geometrical argument as to why the Pareto curve is convex, and show that this is not the case for all parts of the set.
Journal ArticleDOI
A Class of Solutions for Group Decision Problems
TL;DR: Given a set of utility functions defined on a decision space for a group of individuals, the concept of utopia point for the group as well as the group regret of a feasible decision is introduced.