We take advantage of recent advances in optimization methods and computer hardware to identify globally optimal solutions of product line design problems that are too large for complete enumeration. We then use this guarantee of global optimality to benchmark the performance of more practical heuristic methods. We use two sources of data: (1) a conjoint study previously conducted for a real product line design problem, and (2) simulated problems of various sizes. For both data sources, several of the heuristic methods consistently find optimal or near-optimal solutions, including simulated annealing, divide-and-conquer, product-swapping, and genetic algorithms.
Belloni, A., R. Freund, M. Selove, and D. Simester (2008). “Optimizing Product Line Designs: Efficient Methods and Comparisons,” Management Science, 54, 9, p. 1544-1552. doi: 10.1287/mnsc.1080.0864
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This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Management Science, volume 54, issue 9, in 2008 following peer review. The definitive publisher-authenticated version is available online at DOI:10.1287/mnsc.1080.0864