Genetic Algorithms for product design

Product design is increasingly recognized as a critical activity that has a significant impact on the performance of firms. Consequently, when firms undertake a new (existing) product design (redesign) activity, it is important to employ techniques that will generate optimal solutions. As optimal product design using conjoint analysis data is an NP-hard problem, heuristic techniques for its solution have been proposed. This research proposes the use of and evaluates the performance of Genetic Algorithms (GA), an approach which is based on the principles of natural selection, as an alternative procedure for generating ‘good’ (i.e. close to optimal) solutions for the product design problem. The paper focuses on (1) how GA can be applied to the product design problems, (2) determining the comparative performance of GA vis-à-vis the dynamic programming (DP) heuristic in generating solutions to the product design problems, (3) the sensitivity of the GA solutions to variations in parameter choices, and (4) generalizing the results of the dynamic programming heuristic to product designs involving attributes with varying number of levels and studying the impact of alternative attribute sequencing rules.