TY - GEN A1 - Eimecke, Jörgen A1 - Baumert, Katrin A1 - Baier, Daniel T1 - Applying the Repertory Grid Method for Technology Forecasting – Civil Unmanned Aviation Systems for Germany T2 - Management and Production Engineering Review Y1 - 2017 SN - 2082-1344 VL - 8 IS - 3 SP - 22 EP - 30 ER - TY - GEN A1 - Baier, Daniel A1 - Vökler, Sascha T1 - One-stage product-line design heuristics: an empirical comparison T2 - OR Spectrum N2 - Selecting or adjusting attribute-levels (e.g. components, equipments, flavors, ingredients, prices, tastes) for multiple new and/or status quo products is an important task for a focal firm in a dynamic market. Usually, the goal is to maximize expected overall buyers’ welfare based on consumers’ partworths or expected revenue, market share, and profit under given assumptions. However, in general, these so-called product-line design problems cannot be solved exactly in acceptable computing time. Therefore, heuristics have been proposed: Two-stage heuristics select promising candidates for single products and evaluate sets of them as product-lines. One-stage heuristics directly search for multiple attribute-level combinations. In this paper, Ant Colony Optimization, Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing and, firstly, Cluster-based Genetic Algorithm and Max-Min Ant Systems are applied to 78 small- to large-size product-line design problem instances. In contrast to former comparisons, data is generated according to a large sample of commercial conjoint analysis applications ( n = 2,089). The results are promising: The firstly applied heuristics outperform the established ones. KW - Product-line design KW - Conjoint analysis KW - Combinatorial optimization KW - Heuristics Y1 - 2024 U6 - https://doi.org/10.1007/s00291-023-00716-0 SN - 0171-6468 SN - 1436-6304 VL - 46 IS - 1 SP - 73 EP - 107 PB - Springer Berlin Heidelberg ER -