No abstract

Genetic searches often use randomly generated initial populations to maximize diversity and enable a thorough sampling of the design space. While many of these initial configurations perform poorly, the trade-off between population diversity and solution quality is typically acceptable for small-scale problems. Navigating complex design spaces, however, often requires computationally intelligent approaches that improve solution quality. This article draws on research advances in market-based product design and heuristic optimization to strategically construct 'targeted' initial populations. Targeted initial designs are created using respondent-level part-worths estimated from discrete choice models. These designs are then integrated into a traditional genetic search. Two case study problems of differing complexity are presented to illustrate the benefits of this approach. In both problems, targeted populations lead to computational savings and product configurations with improved market share of preferences. Future research efforts to tailor this approach and extend it towards multiple objectives are also discussed.

Industry demands that graduating engineers possess the ability to solve complex problems requiring multidisciplinary approaches and systems-level thinking. Unfortunately, current curricula often focus on analytical approaches to problem solving. Further, adding courses focused solely on engineering design is often unachievable due to the large amount of material covered in today’s undergraduate engineering curricula. Combined, these prevent a comprehensive focus on engineering design education from being realized. To overcome these time and resource constraints, this paper proposes the use of computational modules within current courses. The investigators hypothesize that the modules would eliminate the repetitive analysis barrier in design problems, thus allowing for design-related experiences to be included earlier in the curricula as opposed to postponing it to a capstone experience. Four major hurdles that hinder successful integration of modules in current engineering courses are: a) engaging students such that they will want to use the modules; b) ensuring the modules are easy to use; c) reducing the complexity of deploying the modules into the classroom; and d) providing educational value. To address these issues, this paper treats the design of the modules as a product design problem. This paper presents the redesign process followed to improve two different design modules planned for implementation in the engineering curriculum at North Carolina State University. Additionally, this research indicates that using a formal redesign process enhances a module’s ability to overcome the hurdles listed above.

Genetic searches often use randomly generated initial populations that maximize genetic diversity to thoroughly sample the design space. While many of these initial configurations perform poorly, the tradeoff between population diversity and solution quality is typically acceptable for small design spaces. However, as the design space grows in complexity, computational savings and initial solution quality become more significant considerations. This paper synthesizes advancements from market-based design and heuristic optimization research to strategically construct "targeted" initial populations capable of reducing computational cost and improving final solution quality. Respondent-level utilities from a discrete choice model are used with a price segmentation strategy to efficiently populate designs in a multiobjective environment where designers can explore trade-offs between competing business objectives. Results from an automobile feature packaging problem demonstrate the effectiveness of this approach, and recommendations toward the extent of price segmentation required and future research efforts are offered.

To be competitive in today’s market, firms need to offer a variety of products that appeal to a diverse set of customer needs. Product line optimization provides a simple method to design for this challenge. Using a heterogeneous customer preference model allows the optimization to better explore the diversity in the market. The optimization should also consider aesthetic, engineering, manufacturing, and marketing constraints to ensure the feasibility of the final solution. However, as more constraints are added the difficulty of the optimization increases. There is an opportunity to reduce the difficulty of the optimization by allowing the heterogeneous customer preference model to handle a subset of these constraints termed design prohibitions. Design prohibitions include component incompatibility and dependency. This paper investigates whether design prohibitions should be handled solely in the heterogeneous customer preference model, solely in the optimization formulation, or in both. The effects of including design prohibitions in the creation of a hierarchical Bayes mixed logit model and a genetic algorithm based product line optimization are explored using a bicycle case study.