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, wh en firms undertake a new (existing) product design (redesign) activity , it is important to employ techniques that will generate optimal solu tions. As optimal product design using conjoint analysis data is an NP -hard problem, heuristic techniques for its solution have been propose d. This research proposes the use of and evaluates the performance of Genetic Algorithms (GA), which is based on the principles of natural s election, as an alternative procedure for generating ''good'' (i.e., c lose to optimal) solutions for the product design problem. The paper f ocuses on (1) how GA can be applied to the product design problems, (2 ) determining the comparative performance of GA vis-g-vis the dynamic programming (DP) heuristic (Kohli and Krishnamurti 1987, 1989) in gene rating solutions to the product design problems, (3) the sensitivity o f the GA solutions to variations in parameter choices, and (4) general izing the results of the dynamic programming heuristic to product desi gns involving attributes with varying number of levels and studying th e impact of alternate attribute sequencing rules.