scholarly journals The selection of the supplier as a multicriteria optimization problem

2020 ◽  
Vol 110 (1) ◽  
pp. 85-89
Author(s):  
P Kondratiev ◽  
O Kvashina
Keyword(s):  
Multicriteria Optimization ◽  
Optimization Problem ◽  
Multicriteria Optimization Problem ◽  
Selection Of

Game Theory as a Design Tool

1983 ◽  
Vol 105 (2) ◽  
pp. 165-170 ◽  
Author(s):  
T. L. Vincent
Keyword(s):  
Game Theory ◽  
Multicriteria Optimization ◽  
Optimization Problem ◽  
Design Tool ◽  
Scalar Optimization ◽  
Optimization Task ◽  
Engineering Design Process ◽  
Team Design ◽  
Multicriteria Optimization Problem

Game theory encompasses not only the familiar concept of scalar optimization, but the less known concepts of multicriteria optimization and multiple optimizers as well. This paper examines the role of game theory in the engineering design process. This is done by demonstrating how game theory as a design tool applies beyond scalar optimization to the multicriteria optimization problem. The multicriteria optimization task is examined not only from the perspective of a single designer but from the perspective of team design as well. In the latter case the design problem has been assigned to several designers each responsible for one aspect of the total design.

Robust Design Using Preference Aggregation Methods

2003 ◽  
Author(s):  
Zhihuang Dai ◽  
Michael J. Scott ◽  
Zissimos P. Mourelatos
Keyword(s):  
Robust Design ◽  
Multicriteria Optimization ◽  
Optimization Problem ◽  
Performance Measure ◽  
Preference Aggregation ◽  
Weighted Sum ◽  
Aggregation Method ◽  
Nominal Performance ◽  
Multicriteria Optimization Problem

Robust design is a methodology for improving the quality of a product or process by minimizing the effect of variations in the inputs without eliminating the causes of those variations. In robust design, the best design is obtained by solving a multicriteria optimization problem, trading off the nominal performance against the minimization of the variation of the performance measure. Because these methods often combine the two criteria with a weighted sum or another fixed aggregation strategy, which are known to miss Pareto points, they may fail to obtain a desired design. To overcome this inadequacy, a more comprehensive preference aggregation method is combined into robust design. Two examples are presented to illustrate the effectiveness of the proposed method.

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