Process Models in Design and Development

Micro-level MS/OR models

To recap, the MS/OR category of our framework concerns models which apply mathematical or computer analysis to generate general insights from representative or synthetic situations. While many researchers have developed models of this type on the meso- and macro-levels (as described in forthcoming sections), we found relatively little on the micro-level once work on computational design and design optimisation is excluded. Some examples are given in the next paragraph.

First, Braha and Maimon develop a mathematical model based on the principle that designing is characterised by progressive addition of attributes and relationships. Their model, based on an entropic perspective of design complexity, shows how progress causes an increase in the emerging design's complexity and consequently increases the effort required to progress it further. Second, Zeng and Yao use computer simulation to study the impact of different design strategies within their axiomatic theory of design modelling, which was discussed earlier. This theory suggests that different design solutions emerge through three levers: reformulating the problem; changing the designer's approach to the synthesis steps that occur on each design cycle; and altering the sequence of addressing problems that emerge while designing. Zeng and Yao implement their axiomatic model in an algorithm for generating a finite-element mesh - which they argue is representative of common design problems - and use simulated cases to show that adjusting these levers does indeed result in different solutions. Third, Kazakçi et al. develop a computer model to simulate designing according to the C-K theory principle that designs emerge through the interplay between concepts and knowledge. In this model, graph structures are used to represent the concept and knowledge spaces. These structures evolve through stepwise operations that reflect the steps of designing according to C-K theory. For example, one such operation involves generating a connection between two nodes in K space - this is simulated by selecting the nodes at random. Kazakçi et al. use their simulation to study how attention should be distributed between developing design concepts and undertaking research to develop relevant knowledge. They conclude that emphasising the former may generate a design solution more quickly, while the latter may help to ensure the solution is robust. Finally, another area of work that could contribute to this category is computational creativity, an emerging topic that aims to generate insights into creative activity by simulation of the processes involved. However, such models often focus on non-engineering domains and are thus outside the scope of this article.

To summarise, this is the least populated of the categories in our organising framework. Accordingly, there seems to be an opportunity for further research to apply mathematical and computational modelling to investigate the implications of micro-level models of engineering design activity, such as those discussed in Sect. 3.3.