Genetic algorithms for product design

Article Abstract:

Optimal product design is acknowledged as a critical undertaking that can greatly affect the performance and competitiveness of firms. The use of an adaptive search technique known as Genetic Algorithms (GA) is proposed in solving product design problems. The performance of this approach is evaluated and compared to that of the dynamic programming heuristic of Kohli and Krishnamurti (1987) to product designs with attributes having different numbers of levels and alternative attribute sequencing rules. Results across 192 data sets reveal that the GA technique generates solutions which are 99.13% and 99.92% of the optimal product profile when the goals of the problem are to maximize the share-of-choices and the buyers' welfare, respectively. The figures are statistically better than the solutions identified by the dynamic programming heuristic of Kohli and Krishnamurti (1987, 1989) by 3% and 1% for the two objectives.

Product development, Industrial design

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Note: microcomputer performance of OptPack on Hoffmann's data sets: comparison with Eureka and FABLE

Article Abstract:

OptPack is a laser-type, depth-first search algorithm that examines solutions in lexicographic sequence until an optimum is identified and verified. It initially utilizes two heuristic methods when solving 'easy' problems but eventually resorts to a sorting method equivalent to Johnson's FABLE and an implicit enumeration search when a heuristic solution cannot be validated as optimal. The most significant feature of OptPack is its highly efficient and compact tree-like data structure which files all partial solutions considered in the past. A study subjects the algorithm to microcomputer computation using Hoffmann's data sets and compared to Johnson's FABLE and Hoffmann's Eureka. Results showed that OptPack is significantly faster than FABLE and Eureka on all data sets.

Database searching, Online searching

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