Optimizing Product Line Designs: Efficient Methods and Comparisons

Abstract

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.product line design, conjoint, optimization

An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research

“…14 The simulations conducted suggest that polyhedral question design does well in many domains, particularly those in which heterogeneity and part-worth magnitudes are relatively large. In particular, the polyhedral algorithms hold potential when profile comparisons are more accurate than selfexplicated importance measures and when respondent fatigue is a concern due to a large number of features.…”

“…Combining analytic-center (AC) estimation with Bayesian methods may broaden the scope and applicability of the polyhedral algorithm when respondent heterogeneity and response accuracy in stated choice are both low. Also, 14 Polyhedral "interior-point" algorithms (Fast Polyhedral Adaptive Conjoint Estimation or FastPACE) design questions that quickly reduce the range of feasible part-worths that are consistent with the respondent's choices. The estimation methods employed are hierarchical Bayes and "analytic center", a new estimation procedure that is a by-product of polyhedral question design.…”

A Demand Estimation Procedure for Retail Assortment Optimization with Results from Implementations

“…For the product line design problem, Green and Krieger (1985) use greedy and interchange. Belloni et al (2008) find that for the product line design problem, greedy and interchange together find 98.5% of optimal profits on average for randomly generated problems, and 99.9% for real problems.…”

“…Krieger (1987a,b, 1992), McBride and Zufryden (1988), Dobson and Kalish (1988) and Kohli and Sukumar (1990) extend this line of research. Belloni et al (2008) compare the performance of different heuristics for product line design and find that the greedy and the greedy-interchange heuristics perform extremely well. Smith and Agrawal (2000) use an exogenous demand model and an integer programming formulation of assortment planning.…”

Income distribution, quality differentiation and product line design

“…These studies investigate product line design from a marketing perspective (e.g., Jacob, 2004, 2006;Belloni et al, 2008;Lan Luo, 2010). Orhun (2009) studies optimal product line design when consumers exhibit choice-set-dependent preferences.…”