Heuristics for product-line design using conjoint analysis
Management Science
Optimizing Product Line Designs: Efficient Methods and Comparisons
Management Science
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We develop a holistic and coherent model for the product line design and scheduling problem. Our model incorporates market requirements and financials, design-engineering capabilities, manufacturing costs, and multiple-time dynamics. The solution integrates techniques and concepts from optimal set-covering, resource-constrained job scheduling, dynamic programming, and portfolio optimization to maximize overall profit. The key concept is the decomposition of the problem into two layers. The outer genetic algorithm layer handles resource constraints, scheduling, and financial optimization. The inner mathematical programming layer optimizes product composition as classical set covering. The resulting algorithm efficiently solves problems of larger size and higher complexity than previously possible. Over 250 personnel representing most major Intel groups and many distinct job functions are using the decision support suite (DSS) surrounding the algorithm 21 months after its initial deployment. This DSS serves to integrate a set of previously separate noncommunicating business processes.