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- Bergqvist, Rickard, 1979, et al.
(författare)
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Introduction – A Global Perspective on Dryports
- 2013
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Ingår i: Dryports – A global perspective, challenges and developments in serving hinterlands. - : Ashgate Publishing Limited. - 9781409444244
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 2. |
- Cullinane, Kevin, et al.
(författare)
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The Dry Port Concept – Theory and Practice
- 2012
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Ingår i: Maritime Economics & Logistics. - 1479-2931. ; 14:1, s. 1-13
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Tidskriftsartikel (refereegranskat)abstract
- Driven by the long-term stimulus of increasing worldwide trade and globalisation, the international freight transport industry thrives on continuous change and development, as reflected in managerial, regulatory and technological innovations within the sector. For container ports, in particular, the dynamic nature of such an environment has been most acutely felt in terms of considerable increases in the size of containerships (Cullinane and Khanna, 1999, 2000; Imai et al, 2006), the rationalisation of cargo handling operations in pursuit of greater efficiency (Cordeau et al, 2001; Imai et al, 2001; Zhang et al, 2002; Kim and Moon, 2003; Park and Kim, 2003; Vis and Koster, 2003; Christiansen et al, 2004; Guan and Cheung, 2004; Hansen et al, 2008; Cullinane, 2010), the devolution of port governance (Brooks and Cullinane, 2007) and the need to reorient the marketing of port services for strategic positioning within inherently competitive supply chains, rather than simply within essentially captive hinterlands (Robinson, 2002).
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| 3. |
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| 4. |
- Fan, Shuangrui, et al.
(författare)
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Forecasting Baltic Dirty Tanker Index by Applying Wavelet Neural Networks
- 2013
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Ingår i: Journal of Transportation Technologies. - : Scientific Research Publishing, Inc.. - 2160-0473 .- 2160-0481. ; 3:1, s. 68-87
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Tidskriftsartikel (refereegranskat)abstract
- Baltic Exchange Dirty Tanker Index (BDTI) is an important assessment index in world dirty tanker shipping industry. Actors in the industry sector can gain numerous benefits from accurate forecasting of the BDTI. However, limitations exist in traditional stochastic and econometric explanation modeling techniques used in freight rate forecasting. At the same time research in shipping index forecasting e.g. BDTI applying artificial intelligent techniques is scarce. This analyses the possibilities to forecast the BDTI by applying Wavelet Neural Networks (WNN). Firstly, the characteristics of traditional and artificial intelligent forecasting techniques are discussed and rationales for choosing WNN are explained. Secondly, the components and features of BDTI will be explicated. After that, the authors delve the determinants and influencing factors behind fluctuations of the BDTI in order to set inputs for WNN forecasting model. The paper examines non-linearity and non-stationary features of the BDTI and elaborates WNN model building procedures. Finally, the comparison of forecasting performance between WNN and ARIMA time series models show that WNN has better forecasting accuracy than traditionally used modeling techniques.
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