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

被引：0

作者：

Stelios Bekiros

Nikolaos Loukeris

Nikolaos Matsatsinis

Frank Bezzina

机构：

[1] Athens University of Economics and Business,Department of Accounting and Finance

[2] IPAG Business School,Department of Business Administration

[3] University of Macedonia,School of Production Engineering and Management

[4] Technical University of Crete,Department of Management, Faculty of Economics, Management and Accountancy

[5] University of Malta,undefined

来源：

Computational Economics | 2019年 / 54卷

关键词：

Neural networks; Preference models; Decision support systems; Multi-criteria decision analysis; Data mining; Rough sets; Shipping; C32; C58; G10; G17;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：647 / 667

页数：20

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