Details

Autor(en) / Beteiligte

• Baier, Daniel
• Voekler, Sascha

Titel

One-stage product-line design heuristics: an empirical comparison

Ist Teil von

• OR Spectrum, 2024-03, Vol.46 (1), p.73-107

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2024

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.