Customer Satisfaction Prediction in the Shipping Industry with Hybrid Meta-heuristic Approaches

Stelios Bekiros, Nikolaos Loukeris, Nikolaos Matsatsinis () and Frank Bezzina ()
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Nikolaos Matsatsinis: Technical University of Crete
Frank Bezzina: University of Malta

Computational Economics, 2019, vol. 54, issue 2, No 8, 647-667

Abstract: Abstract 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.

Keywords: Neural networks; Preference models; Decision support systems; Multi-criteria decision analysis; Data mining; Rough sets; Shipping (search for similar items in EconPapers)
JEL-codes: C32 C58 G10 G17 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10614-018-9842-5

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