Design process sequencing with competent genetic algorithms

In product design, it is critical to perform project activities in an appropriate sequence. Otherwise, essential information will not be available when it. is needed, and activities that depend on it will proceed using assumptions instead. Later when the real information is finally available, comparing it with the assumptions made often precipitates a cascade of rework, and thus cost and schedule overruns for the project. Information flow models have been used to sequence the engineering design process to minimize feedback and iteration, i.e., to maximize the availability of real information where assumptions might otherwise be made instead. In this paper we apply Genetic Algorithms (GAs) to an information flow model to find an optimized sequence for a set of design activities. The optimality of a solution depends on the objective of rearrangement. In an activity sequencing context, objectives vary: reducing iteration/feedback, increasing, concurrency, reducing development lead-time and cost, or some combination of these. We adopt a matrix-based representation scheme, the design structure matrix (DSM), for the information flow models. Our tests indicate that certain DSM characteristics (e.g., size, sparseness, and sequencing objective) cause serious problems for simple Genetic Algorithm (SGA) designs. To cope with the SGA deficiency, we investigate the use of a competent GA: the ordering messy GA (OmeGA). Tests confirm the superiority of the OmeGA over a SGA for hard DSM problems. Extensions enhancing the efficiency of both a SGA and the OmeGA, in particular, niching and hybridization with a local search method, are also investigated.