Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/64C9C9B2-7B3D-4CCD-B96E-DF5A28348FAB | - |
| Identifier | https://doi.org/10.1007/s10614-018-9842-5 | - |
| Identifier | https://link.springer.com/article/10.1007/s10614-018-9842-5 | - |
| Language | en | - |
| Extent | 21 pages | en |
| Title | Customer satisfaction prediction in the shipping industry with hybrid meta-heuristic approaches | en |
| Creator | Bekiros, Stelios | en |
| Creator | Loukeris Nikolaos | en |
| Creator | Λουκερης Νικολαος | el |
| Creator | Matsatsinis Nikolaos | en |
| Creator | Ματσατσινης Νικολαος | el |
| Creator | Bezzina, Frank | en |
| Publisher | Springer Nature [academic journals on nature.com] | en |
| Content Summary | Optimization and prediction of customer satisfaction in the shipping industry impacts immensely upon strategic planning and consequently on the targeted market share of a corporation. In shipping industry, accurate measures of customer satisfaction are usually very cumbersome to elaborate. In this work we aim to reveal the most effective optimization methods, employing artificial intelligence approaches such as rough sets, neural networks, advanced classification methods as well as multi-criteria analysis under a comparative framework vis-à-vis their forecasting performance. | 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 | 2019-11-14 | - |
| Date of Publication | 2019 | - |
| Subject | Data mining | en |
| Subject | Decision support systems | en |
| Subject | Multi-criteria decision analysis | en |
| Subject | Neural networks | en |
| Subject | Preference models | en |
| Subject | Rough sets | en |
| Subject | Shipping | en |
| Bibliographic Citation | S. Bekiros, N. Loukeris, N. Matsatsinis and F. Bezzina, "Customer satisfaction prediction in the shipping industry with hybrid meta-heuristic approaches," Comput. Econ., vol. 54, no. 2, pp. 647-667, Aug. 2019. doi: 10.1007/s10614-018-9842-5 | en |
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