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Optimizing Product Line Designs: Efficient Methods and Comparisons

Fuqua School of Business, Duke University, Durham, North Carolina 27708
MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
abn5{at}duke.edu
rfreund{at}mit.edu
mselove{at}mit.edu
simester{at}mit.edu

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.

Key Words: product line design; conjoint; optimization
History: Received: February 22, 2005;