A Heuristic Approach to Product Design
提出一种动态规划启发式算法，用于在竞争市场中寻找多属性新产品的最优配置，该算法在模拟中平均达到最优方案98.2%的选择份额，且计算效率优于拉格朗日松弛法。
A dynamic-programming heuristic is described to find approximate solutions to the problem of identifying a new, multi-attribute product profile associated with the highest share-of-choices in a competitive market. The input data consist of idiosyncratic multi-attribute preference functions estimated using conjoint or hybrid-conjoint analysis. An individual is assumed to choose a new product profile if he/she associates a higher utility with it than with a status-quo alternative. Importance weights are assigned to individuals to account for differences in their purchase and/or usage rates and the performance of a new product profile is evaluated after taking into account its cannibalization of a seller's existing brands. In a simulation with real-sized problems, the proposed heuristic strictly dominates an alternative lagrangian-relaxation heuristic in terms of both computational time and approximation of the optimal solution. Across 192 simulated problems, the dynamic-programming heuristic identifies product profiles whose share-of-choices, on average, are 98.2% of the share-of-choices of the optimal product profile, suggesting that it closely approximates the optimal solution.