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Experimental and robust design

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Abstract

The complexity of business problems, organizations, operational and service systems, the number of variables they involve, as well as the often chaotic environment to which they are subjected, make it difficult to use prior knowledge (in the form of mathematical models, for example) to construct and calibrate these systems. In these cases, experimentation is an important approach to generate knowledge which can be used for effective analysis and decision making. When a product is put to use, the number of intervening variables may be too large, some of which may also be uncontrollable. Further, experiments are usually costly; there may be many variables and potentially a great deal of experimental variation and errors, making the experimental results obtained difficult to compare and analyse in a statistically acceptable manner. For such situations, experimental design, when it is properly used, provides a set of consistent procedures and principles for collecting data so that an estimate of relationships between one set of variables, called explanatory variables, and another, called dependent variables, can be performed (even if there are experimental errors). For example, we might seek to build a relationship between supply delay (the dependent variable) and a number of explanatory variables such as the number of transport trucks (which can be controlled), weather conditions and traffic intensity (which cannot be controlled).

Keywords

Factorial Experiment Central Composite Design Robust Design Noise Factor Fractional Factorial Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Charles S. Tapiero 1996

Authors and Affiliations

  1. 1.Ecole Supérieure des Sciences Economiques et CommercialesParisFrance

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