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A fuzzy data meta training system for ranking hub container terminals

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  • Caggiani, Leonardo
  • Iannucci, Giuseppe
  • Ottomanelli, Michele
  • Tangari, Lucia
  • Sassanelli, Domenico

Abstract

The potential and critical aspects of any transport service can be highlighted through the estimation of appropriate performance indicators of the examined system. Commonly, container terminal analysis is based first on the evaluation and comparison of quantitative parameters that describe the level of service of the terminal and, on the other side by means of performance indicators related to terminal productivity. In this paper a Fuzzy Inference System for evaluation of a synthetic performance indicator is proposed. This tool could help planners and managers in terminals performances analysis and ranking as well as in assessing the effects of possible intervention on the systems. The proposed approach is suitable in the case of hub container ports. In fact this system is characterised by significant uncertainties and it is not always governed by certain rules, rational behaviour, so that it cannot be easily represented by traditional mathematical techniques and models. In our opinion, could be convenient to define the values of the considered parameters by explicitly define them in an approximate way, that is to say by fuzzy sets.

Suggested Citation

  • Caggiani, Leonardo & Iannucci, Giuseppe & Ottomanelli, Michele & Tangari, Lucia & Sassanelli, Domenico, 2012. "A fuzzy data meta training system for ranking hub container terminals," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 51, pages 1-5.
    • Handle: RePEc:sot:journl:y:2012:i:51:p:5
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    File URL: http://hdl.handle.net/10077/6120
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    References listed on IDEAS

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    1. Cullinane, Kevin & Wang, Teng-Fei & Song, Dong-Wook & Ji, Ping, 2006. "The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(4), pages 354-374, May.
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