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Baier, Daniel ; Vökler, Sascha:
Product-Line Design Using Cluster-Based Genetic Algorithms and Tabu Search.
In: Okada, Akinori ; Shigemasu, Kazuo ; Yoshino, Ryozo ; Yokoyama, Satoru
(Hrsg.):
Facets of Behaviormetrics : The 50th Anniversary of the Behaviormetric Society. -
Singapore
: Springer
,
2023
. - S. 3-21
ISBN 978-981-99-2239-0
DOI: https://doi.org/10.1007/978-981-99-2240-6_1
Abstract
Selecting adequate attribute-levels (e.g., components, ingredients, materials, prices, qualities) for new and/or existing products is an important task for marketeers. The goal is to maximize a focal firm’s overall revenue or profit. The typical knowledge base consists of customers’ attribute-level partworths, marginal contributions, and descriptions of own and competing status quo products. However, since these so-called product-line design problems are known to be NP-hard, they often cannot be solved exactly. Instead, heuristics have to be applied. In this paper, we give an overview on proposed solution methods. Moreover, we apply two recent propositions—Cluster-Based Genetic Algorithms (CGA) and Tabu Search (TS)—to a sample of 460 small (up to about 10 possible solutions)-to-large-size problems (more than 10 possible solutions). The results are promising: Especially CGA solves small-size problems accurately and in acceptable computing time (within seconds), the latter even when applied to medium- and large-size problems.
Weitere Angaben
| Publikationsform: | Aufsatz in einem Buch |
|---|---|
| Begutachteter Beitrag: | Ja |
| Institutionen der Universität: | Fakultäten > Rechts- und Wirtschaftswissenschaftliche Fakultät > Fachgruppe Betriebswirtschaftslehre > Lehrstuhl Betriebswirtschaftslehre XIV - Marketing und Innovation > Lehrstuhl Betriebswirtschaftslehre XIV - Marketing und Innovation - Univ.-Prof. Dr. Daniel Baier |
| Titel an der UBT entstanden: | Ja |
| Themengebiete aus DDC: | 300 Sozialwissenschaften > 330 Wirtschaft |
| Eingestellt am: | 07 Feb 2024 06:15 |
| Letzte Änderung: | 07 Feb 2024 06:15 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/88500 |