Physical programming - Effective optimization for computational design

Messac, Achille

doi:10.2514/6.1996-1427

Cited by 58 publications

(92 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The physical programming method has the following advantages (Messac 1996;Tian and Zuo 2006). Equation (1) the iterative weight-adjusting process is eliminated, thus the computational burden is substantially reduced; (2) The designers only need to specify the physically meaningful boundary values for each design objective, not those meaningless weights, which makes this approach very easy to use;…”

Section: The Physical Programming Methodsmentioning

confidence: 99%

“…Typically, only Class 1-S functions (to be minimized) and Class 2-S functions (to be maximized) are the soft class functions that we have, and the physical programming problem model can be formulated as follows (Messac 1996;Tian and Zuo 2006):…”

Section: The Physical Programming Methodsmentioning

confidence: 99%

“…What the designer needs to do is just to specify ranges of different degrees of desirability (highly desirable, desirable, tolerable, undesirable, highly undesirable, and unacceptable) for the class function of each objective. Given the specified boundary values, the class function for a design objective can be constructed using the method developed by Messac (1996).…”

Section: The Physical Programming Methodsmentioning

confidence: 99%

“…However, the disadvantage of single-objective optimization is that we cannot systematically investigate the tradeoff between the optimization objectives and find the optimal solution that best represents the decision maker's preference on the optimization objectives. Physical programming is an effective multi-objective optimization approach developed in 1996 (Messac 1996), and it has proved its effectiveness in addressing a wide array of multi-objective optimization problems in the field of structural optimization, product family design, control, robust design, reliability optimization, etc. (Huang et al 2005b;Tian and Zuo 2006;Tian et al 2008).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Condition based maintenance optimization considering multiple objectives

Tian

Lin

2009

J Intell Manuf

View full text Add to dashboard Cite

In condition based maintenance (CBM) optimization, the main optimization objectives include maximizing reliability and minimizing maintenance costs, which are often times conflicting to each other. In this work, we develop a physical programming based approach to deal with the multi-objective condition based maintenance optimization problem. Physical programming presents two major advantages: (1) it is an efficient approach to capture the decision makers' preferences on the objectives by eliminating the iterative process of adjusting the weights of the objectives, and (2) it is easy to use in that decision makers just need to specify physically meaningful boundaries for the objectives. The maintenance cost and reliability objectives are calculated based on proportional hazards model and a control limit CBM replacement policy. With the proposed approach, the decision maker can systematically and efficiently make good tradeoff between the cost objective and reliability objective. An example is used to illustrate the proposed approach.

show abstract

Section: The Physical Programming Methodsmentioning

confidence: 99%

Section: The Physical Programming Methodsmentioning

confidence: 99%

Section: The Physical Programming Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Condition based maintenance optimization considering multiple objectives

Tian

Lin

2009

J Intell Manuf

View full text Add to dashboard Cite

show abstract

“…Multiobjective optimization is a well-known, well-accepted, means to quantify tradeoffs between competing design objectives (Frischknecht et al 2011;Kasprzak and Lewis 2000;Messac 1996), and as such forms a fundamental building block for the methodology presented in Section 2 of this paper. One pertinent application of multiobjective optimization in the context of this paper is that of identifying a set of non-dominated designs-Pareto frontier (Gurnani and Lewis 2008;Messac and Mattson 2004;Todoroki and Sekishiro 2008).…”

Section: Multiobjective Optimizationmentioning

confidence: 99%

A method for developing systems that traverse the Pareto frontiers of multiple system concepts through modularity

Lewis

Mattson

2011

Struct Multidisc Optim

View full text Add to dashboard Cite

Natural changes in customer needs over time often necessitate the development of new systems that satisfy the new needs. In a previous work by the authors, a 5-step multiobjective optimization-based method was presented to identify systems that anticipate, account for, and allow for these changes by moving from one Pareto design to another through module addition. Recognizing the potential for changes in needs to exceed the limits of a single Pareto frontier, the present paper introduces important advancements that extend development to modules connecting multiple disparate system concepts. As such, the search for suitable system designs is extended from a Pareto frontier that characterizes one system concept to a Pareto frontier that characterizes a set of system concepts. An expanded methodology is described, and a tri-objective hurricane and flood resistant residential structure example is used to demonstrate the method. The authors conclude that the developed method provides a new methodology for selecting platform and module designs in the presence of multiple system concepts, and is capable of identifying a set of modular system designs that are well-suited to satisfy changing needs over time.

show abstract

Multiresponse problems: desirability and other optimization approaches

Costa

Lourenço

2016

Journal of Chemometrics

View full text Add to dashboard Cite

A diversity of multiresponse optimization methods has been introduced in the literature; however, their performance has not been thoroughly explored, and only a classical desirability‐based criterion has been commonly used. With the aim of contributing to help practitioners in selecting an effective criterion for solving multiresponse optimization problems developed under the response surface methodology framework, and thus to find compromise solutions that are technically and economically more favorable, the working ability of several easy‐to‐use criteria is evaluated and compared with that of a theoretically sound method. Four case studies with different numbers and types of responses are considered. Less‐sophisticated criteria were able to generate solutions similar to those generated by sophisticated methods, even when the objective is to depict the Pareto frontier in problems with conflicting responses. Two easy‐to‐use criteria that require less‐subjective information from the user yielded solutions similar to those of a classical desirability‐based criterion. Preference parameters range and increment impact on optimal solutions were also evaluated.

show abstract

Physical programming - Effective optimization for computational design

Cited by 58 publications

References 14 publications

Condition based maintenance optimization considering multiple objectives

Condition based maintenance optimization considering multiple objectives

A method for developing systems that traverse the Pareto frontiers of multiple system concepts through modularity

Multiresponse problems: desirability and other optimization approaches

Contact Info

Product

Resources

About