Multiobjective Optimal Placement of Convectively and Conductively Cooled Electronic Components on Printed Wiring Boards

1999 ◽  
Vol 122 (2) ◽  
pp. 152-159 ◽  
Author(s):  
Nestor V. Queipo ◽  
Guy F. Gil
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Optimal Placement ◽  
Printed Wiring Boards ◽  
Multiattribute Utility ◽  
Utility Analysis ◽  
Electronic Components ◽  
Multiattribute Utility Analysis ◽  
Solution Methodology

This paper presents a solution methodology for the optimal placement of convectively and conductively air-cooled electronic components on planar printed wiring boards considering thermal and electrical/cost design objectives. The methodology combines the use of a heat transfer solver for the prediction of the temperature distribution among the electronic components and a genetic algorithm for the adaptive search of optimal or near optimal solutions and a multiobjective optimization strategy (Pareto optimization and multiattribute utility analysis). After proper validation of the elements of the solution methodology (heat transfer solver/genetic algorithm) in isolation, the methodology under consideration is tested using a placement problem (case study) that considers as optimization criteria the minimization of an estimate of the failure rate of the system of components due to thermal overheating (via an Arrhenius relation) and the minimization of the total wiring length given some interconnectivity requirements. Results corresponding to the case study are presented and discussed for both Pareto optimization and multiattribute utility analysis. [S1043-7398(00)00801-X]

Subjective Design Evaluation With Multiple Attributes

1990 ◽  
Author(s):  
Deborah L. Thurston
Keyword(s):  
Automotive Industry ◽  
The United States ◽  
Quantitative Information ◽  
Design Decision ◽  
Utility Analysis ◽  
Materials Selection ◽  
Design Alternatives ◽  
Multiattribute Utility Analysis ◽  
Alternative Designs

Abstract A formal methodology is presented which may be used to evaluate design alternatives in the iterative design/redesign process. Deterministic multiattribute utility analysis is used to compare the overall utility or value of alternative designs as a function of the levels of several performance characteristics of a manufactured system. The evaluation function reflects the designers subjective preferences. Sensitivity analysis provides quantitative information as to how a design should be modified in order to increase its utility to the design decision maker. Improvements in one or more areas or performance and tradeoffs between attributes which would increase desirability of a design most may be quantified. A case study of materials selection and design in the automotive industry is presented. The methodology was applied to 6 automotive companies in the United States and Europe, and results are used to illustrate the steps followed in application.

Disassembly Process Planning Tradeoffs for Product Maintenance

2010 ◽  
Author(s):  
Sara Behdad ◽  
Deborah Thurston
Keyword(s):  
Linear Programming ◽  
Heating System ◽  
Solar Heating ◽  
Multiattribute Utility ◽  
Utility Analysis ◽  
Disassembly Sequence Planning ◽  
Disassembly Process ◽  
Disassembly Sequence ◽  
Multiattribute Utility Analysis ◽  
Disassembly Time

The problem addressed in this paper is disassembly sequence planning for the purposes of maintenance or component upgrading, which is an integral part of the remanufacturing process. This involves disassembly, component repair or replacement, and reassembly. Each of these steps incurs cost as well as the probability of damage during the process. This paper presents a method for addressing these tradeoffs, as well as the uncertainty associated with them. A procedure for identifying the best sequence of disassembly operations for maintenance and/or component upgrade is presented. It considers both disassembly and reassembly costs and uncertainties. Graph-based integer linear programming combined with multiattribute utility analysis is employed to identify the best set of tradeoffs among (a) disassembly time (and resulting cost) under uncertainty, (b) the probability of not incurring damage during disassembly, (c) reassembly time (and resulting cost) and (d) the probability of not incurring damage during reassembly. An example of a solar heating system is used to illustrate the method.

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