What is Design Exploration?

We all want to design the best and most desirable products. Not surprisingly, the way we design them (i.e., the steps we take to conceive, develop, and commercialize a product) has a big influence on how desirable those products become.

Informally we can think of Design Exploration as a particular way of arriving at a desirable and often optimal design solution. Formally, Design Exploration is the human-driven, often computer-assisted, divergent/convergent process used to evolve and investigate multidisciplinary design space with the intent of design discovery and to inform decision making throughout the design process.

(more (put this somewhere else?)

What makes Design Exploration different than Design Optimization?

As described below, the essential difference between design optimization and design exploration is the assumptions about what is known before the search process begins.

Design optimization strategies have two distinct parts; formulate and converge. Here it is assumed that the problem can be formulated before the search and convergence begins. Design exploration strategies, on the other hand, are based on the belief that the problem formulation evolves during the process of searching and converging, thus ultimately leading to a more informed optimal solution. In this way, design exploration is both divergent and convergent.

Design optimization depends on a well-posed optimization problem formulation, which generally includes (i) a well-defined objective function, (ii) inequality and equality constraints, and (iii) the expression of stakeholder preference, all of which are likely to be multidisciplinary in nature. In an arguably real way, such a problem formulation predefines the optimum solution, thereby allowing the mathematical rigor of the optimization to lead to the optimum design by an iterative, computational search.

Design exploration, on the other hand, assumes that the optimal design is initially unknown and initially uncharacterizable. The process of design exploration discovers design conditions and little-by-little (often through some form of experimentation) characterizes what an optimal design looks like. Once this is known, the final solution can then be found through a convergent design optimization algorithm.

We view design exploration as an evolutionary step beyond the traditional convergent process of design optimization largely because it encourages and allows the human to remain in the loop during the divergent/convergent process.