The Design of Open Engineering Systems Lab

University at Buffalo - The State University of New York

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Optimization of Vehicles and Platforms Through Engineering and Marketing Integration

Research Area: Research Publication Year: 2006
Type of Publication: Technical Report Keywords: Optimization of Vehicles and Platforms Through Engineering and Marketing Integration
Authors: Ferguson, Scott; Lewis, Kemper; Donndelinger, Joseph
Traditionally, preliminary vehicle design has been a highly iterative process, largely because it is difficult to develop designs that are both highly desirable and technically feasible. The levels of iteration and difficulty increase substantially when multiple vehicles sharing common design elements are designed simultaneously. In this paper, we present a framework for rapidly generating highly desirable and technically feasible preliminary designs for both individual products and product families. Feasibility is assessed using a Technical Feasibility Model (TFM) and desirability is estimated using a market model based on the S-Model. The market model spans multiple market segments with segment-specific customer preferences and is parameterized using sales volume and performance data from existing vehicles. Optimal vehicle designs are generated within each individual market segment by simultaneously exercising the marketing model and the TFM. The results of these optimizations reveal opportunities for potentially improving the customer-perceived value of the vehicles currently offered for sale in each market segment. The design variables for the individually optimized designs are then investigated for commonality using the performance-to-design space mapping capabilities in the TFM. This analysis shows that in design space, some variables are already shared between the individually optimized vehicles; additional candidates for communization are also identified. A product platform optimization problem is then formulated to investigate tradeoffs between the customerperceived value of the designs and commonality between the designs. The results of this optimization suggest opportunities for increasing commonality between designs with minimal decreases in customer-perceived value.
Multidisciplinary Analysis and Optimization Conference