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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/59F05E17-25CC-4345-A6E9-FAB3B463B02B | - |
| Identifier | https://doi.org/10.1016/j.indmarman.2013.03.002 | - |
| Language | en | - |
| Extent | 11 pages | en |
| Title | Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variable | en |
| Creator | Matsatsinis Nikolaos | en |
| Creator | Ματσατσινης Νικολαος | el |
| Creator | Tsafarakis, Stelios, 1977- | en |
| Creator | Charalampos Saridakis | en |
| Creator | Baltas George | en |
| Publisher | Elsevier | en |
| Content Summary | This article presents an artificial intelligence-based solution to the problem of product line optimization. More specifically, we apply a new hybrid particle swarm optimization (PSO) approach to design an optimal industrial product line. PSO is a biologically-inspired optimization framework derived from natural intelligence that exploits simple analogues of collective behavior found in nature, such as bird flocking and fish schooling. All existing product line optimization algorithms in the literature have been so far applied to consumer markets and product attributes that range across some discrete values. Our hybrid PSO algorithm searches for an optimal product line in a large design space which consists of both discrete and continuous design variables. The incorporation of a mutation operator to the standard PSO algorithm significantly improves its performance and enables our mechanism to outperform the state of the art Genetic Algorithm in a simulated study with artificial datasets pertaining to industrial cranes. The proposed approach deals with the problem of handling variables that can take any value from a continuous range and utilizes design variables associated with both product attributes and value-added services. The application of the proposed artificial intelligence framework yields important implications for strategic customer relationship and production management in business-to-business markets. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-11-03 | - |
| Date of Publication | 2013 | - |
| Subject | Hybridization, Vegetable | en |
| Subject | Plants--Hybridization | en |
| Subject | plant hybridization | en |
| Subject | hybridization vegetable | en |
| Subject | plants hybridization | en |
| Subject | Finance, Personal--Marketing | en |
| Subject | Marketing of financial planning services | en |
| Subject | financial planners marketing | en |
| Subject | finance personal marketing | en |
| Subject | marketing of financial planning services | en |
| Bibliographic Citation | S. Tsafarakis, C. Saridakis, G. Baltas, N. Matsatsinis ," Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variables," Ind.Marketing Man.,vol.42 ,no.4,pp. 496-506,Ma. 2013.doi:10.1016/j.indmarman.2013.03.002 | en |
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