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Product-Line Design Using Cluster-Based Genetic Algorithms and Tabu Search

Title data

Baier, Daniel ; Vökler, Sascha:
Product-Line Design Using Cluster-Based Genetic Algorithms and Tabu Search.
In: Okada, Akinori ; Shigemasu, Kazuo ; Yoshino, Ryozo ; Yokoyama, Satoru (ed.): Facets of Behaviormetrics : The 50th Anniversary of the Behaviormetric Society. - Singapore : Springer , 2023 . - pp. 3-21
ISBN 978-981-99-2239-0
DOI: https://doi.org/10.1007/978-981-99-2240-6_1

Abstract in another language

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.

Further data

Item Type: Article in a book
Refereed: Yes
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XIV - Marketing and Innovation > Chair Business Administration XIV - Marketing and Innovation - Univ.-Prof. Dr. Daniel Baier
Result of work at the UBT: Yes
DDC Subjects: 300 Social sciences > 330 Economics
Date Deposited: 07 Feb 2024 06:15
Last Modified: 07 Feb 2024 06:15
URI: https://eref.uni-bayreuth.de/id/eprint/88500