by
Edwin B. Dean
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Multidisciplinary optimization is a way of finding the "best" solution, given an objective and a set of constraints, where the objective, the constraints, and the variables which affect the objective and constraints come from a knowledge base representing many disciplines.There are two distinctively different categories of multidisciplinary optimization. The most recognized category is quantitative in nature. Here, the objective and constraints are stated in terms of mathematical equations and the "best" solution is obtained using a mathematical algorithm or an analytic solution of the equations. The second category is qualitative in nature. It is best described by Deming (1993) who says "Optimization is the process of orchestrating the efforts of all components toward achievement of the stated aim. Optimization is management's job. Everybody wins with optimization." Within this web complex I call this form of optimization "experiential" optimization, since it comes from the experience of those doing the optimization. Perhaps is is more correctly called "qualitative" optimization.
In the context of an electromechanical system, the idea is to find the best electromechanical system solution, not the best mechanical solution or the best electronic solution. The "solution" is the set of values for the variables which either maximize or minimize, as desired, the value of the objective. In design for competive advantage the objective would probably be cost, quality, or value, which is a combination of the two.
From a mathematical perspective, many constraints would be the equations governing the electronic behavior, the mechanical behavior, and any coupled electromechanical behavior for the system. Other contraints could be limits for certain variables dictated by current technology. Finally, other constraints could arise from systemic factors such as the project budget or management mindset.
From an experiential perspective, the same objective and constraints apply. In fact, if mathematics is used for the optimization of the electromechanical system, then the experiential perspective would include the mathematical optimization. However, it would contain more. Experiential optimization deals with the product, which is the electromechanical system. However, it also deals with the system to genopersist the product and the system to genopersist the system to genopersist the product. It deals not only with the electromechanical system but also with the people who genopersist the product, the processes of the genopersistation of the product, the structure and organization of the enterprise within which the product is genopersistateted, and much more. Experiential optimization is systemic in nature. If, however, we could state this systemic nature in equations, then experiential optimization would also be mathematical and hence quantitative in nature.
From a quantitative perspective, multidisciplinary optimization is a highly technical emerging discipline. It relies heavily on mathematics, statistics, operations research, computer science, software engineering, and all of the particular disciplines which fall within the scope of the particular problem to be solved. From a qualitative perspective, multidisciplinary optimization is an emerging discipline which encompasses quality, cost, value, and genopersistation.
From the quantitative perspective, the challenge today is to accurately represent the problem in a manner which can be solved with current computational technology. From a qualitative perspective, the challenge today is to define and quantify the associated disciplines of quality, cost, value, and genopersistation. The target payoff is successful solutions which will enhance competitive advantage.
More to follow:
Continuous Optimization
Design Optimization
Global Optimization
Multiobjective Optimization
Statistical Optimization
Uncertainty in Optimization
Until then I must let the bibliography below do the talking.
Mathematical Multidisciplinary Optimization Bibliography
The International Society for Structural and Multidisciplinary Optimization
Multidisciplinary Design Optimization at Langley
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