Abstract
System design has been facing the challenges of incorporating complex dependencies between individual entities into design formulations. For example, while the decision-based design framework successfully integrated customer preference modeling into optimal design, the problem was formulated from a single entity's perspective, and the competition between multiple enterprises was not considered in the formulation. In recent years, network science has offered several solutions for studying interdependencies in various system contexts. However, efforts have primarily focused on analysis (i.e., the forward problem). The inverse problem still remains: How can we achieve the desired system-level performance by promoting the formation of targeted relations among local entities? We answer this question by developing a network-based system design framework. This framework uses network representations to characterize dependencies between individual entities in complex systems and integrate these representations into design formulations to find optimal decisions for the desired performance of a system. To demonstrate its utility, we applied this framework to the design for market systems with a case study on vacuum cleaners. The objective is to promote the formation of a product's inter-brand triadic competitions (closed triangles involving three products from different brands) by optimizing its suction power and weight while keeping prices unchanged. We solve this problem by integrating an exponential random graph model (ERGM) with a genetic algorithm. The results indicate that the new designs can effectively increase the median number of inter-brand triadic competitions that specific vacuum cleaner models participate in as desired.
