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

Author

Listed:
  • Stelios Bekiros

    (Athens University of Economics and Business
    IPAG Business School)

  • Nikolaos Loukeris

    (University of Macedonia)

  • Nikolaos Matsatsinis

    (Technical University of Crete)

  • Frank Bezzina

    (University of Malta)

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.

Suggested Citation

  • Stelios Bekiros & Nikolaos Loukeris & Nikolaos Matsatsinis & Frank Bezzina, 2019. "Customer Satisfaction Prediction in the Shipping Industry with Hybrid Meta-heuristic Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 647-667, August.
    • Handle: RePEc:kap:compec:v:54:y:2019:i:2:d:10.1007_s10614-018-9842-5
    DOI: 10.1007/s10614-018-9842-5
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    References listed on IDEAS

    as
    1. N. Loukeris & I. Eleftheriadis & E. Livanis, 2016. "The Portfolio Heuristic Optimisation System (PHOS)," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 627-648, December.
    2. Grigoroudis, E. & Siskos, Y., 2002. "Preference disaggregation for measuring and analysing customer satisfaction: The MUSA method," European Journal of Operational Research, Elsevier, vol. 143(1), pages 148-170, November.
    3. Nikolaos Loukeris & Iordanis Eleftheriadis, 2015. "Further Higher Moments in Portfolio Selection and A Priori Detection of Bankruptcy, Under Multi‐layer Perceptron Neural Networks, Hybrid Neuro‐genetic MLPs, and the Voted Perceptron," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 20(4), pages 341-361, October.
    4. R. Slowinski & C. Zopounidis, 1995. "Application of the Rough Set Approach to Evaluation of Bankruptcy Risk," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(1), pages 27-41, March.
    5. Pawlak, Zdzislaw, 1997. "Rough set approach to knowledge-based decision support," European Journal of Operational Research, Elsevier, vol. 99(1), pages 48-57, May.
    6. Stelios Bekiros & Nikolaos Loukeris & Iordanis Eleftheriadis & Christos Avdoulas, 2019. "Tail-Related Risk Measurement and Forecasting in Equity Markets," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 783-816, February.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Neural networks; Preference models; Decision support systems; Multi-criteria decision analysis; Data mining; Rough sets; Shipping;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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