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Warianty tytułu
Języki publikacji
Abstrakty
This model optimizes port hinterland intermodal refrigerated container flows , considering both cost and quality degradation, which is distinctive from the previous literature content in a way that it quantifies the influence of carbon dioxide (CO2) emission in different setting temperature on intermodal network planning. The primary contribution of this paper is that the model is beneficial not only to shippers and customers for the novel service design, but also offer , for policy-makers of the government, insights to develop inland transport infrastructures in consideration of intermodal transportation. The majority of models of multimodal system have been established with an objective of cost minimization for normal commodities. As the food quality is possible to be influenced by varying duration time required for the storage and transportation, and transportation accompanied with refrigeration producing more CO2 emission, this paper aims to address cost minimization and quality degradation minimization within the constraint of CO2 footprint. To achieve this aim, we put the quality degradation model in a mixed-integer linear programming model used for intermodal network planning for cold chain. The example of Dalian Port and Yingkou Port offer insight into trade-offs between transportation temperature and transport mode considering CO2 footprint. Furthermore, the model can offer a useful reference for other regions with the demand for different imported food, which requires an uninterrupted cold chain during the transportation and storage.
Czasopismo
Rocznik
Tom
Strony
61--69
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
autor
- Dalian University of Technology Linggong Road, 116024 Dalian China
autor
- Dalian University of Technology Linggong Road, 116024 Dalian China
autor
- Dalian University of Technology Linggong Road, 116024 Dalian China
autor
- Dalian University of Technology Linggong Road, 116024 Dalian China
Bibliografia
- 1. FAO: FAO Statistical Yearbook 2014 (FAO ed.).
- 2. H. Min : International intermodal choices via chanceconstrained goal programming. Transportation Research Part A General. 25(6), 1991, pp.351-362.
- 3. FIannone and S Thore: An economic logistics model for the multimodal inland distribution of maritime containers. International Journal of Transport Economics. 37, 2010 , pp. 281-326.
- 4. W.F. Wang and W.Y. Yun : Scheduling for inland container truck and train transportation. International Journal of Production Economics. 143, 2013, pp. 349-356.
- 5. R . Zhang, W.Y. Yun , H. Kopfer: Heuristic-based truck scheduling for inland container transportation. Or Spectrum. 32 , 2010, pp. 787-808.
- 6. C. Liao, P. Tseng and C. Lu: Comparing carbon dioxide emissions of trucking and intermodal container transport in Taiwan. Transportation Research Part D : Transport and Environment:,14, 2009, pp. 493-496.
- 7. X.J. Yang, J. Low and L.C. Tang: Analysis of intermodal freight from China to Indian Ocean: A goal programming approach. Journal of Transport Geography, 19, 2011, pp. 515-527.
- 8. R.Y. Zhang, W.Y. Yun , I.K. Moon: Modelling and optimization of a container drayage problem with resource constraints. International Journal of Production Economics,133, 2011, pp. 351-359.
- 9. J. Lam and Y. Gu : A market-oriented approach for intermodal network optimisation meeting cost, time and environmental requirements. International Journal of Production Economics,171, 2011, pp. 266-274.
- 10. M. Rahimi, A. Asef-Vaziri and R. Harrison: An Inland Port Location-Allocation Model for a Regional Intermodal Goods Movement System. Maritime Economics & Logistics, 10 , 2010, pp. 362-379.
- 11. W.B. Fitzgerald, O. Howitt , I.J. Smith, et al. : Energy use of integral refrigerated containers in maritime transportation. Energy Policy,39, 2011, pp. 1885-1896.
- 12. K. Vankerschaver, F. Willocx , C. Smout, et al. : Mathematical modelling of temperature and gas composition effects on visual quality changes of cut endive. Journal of Food Science, 61,1998, pp. 613.
- 13. K. McDonald and D.W. Sun : Predictive food microbiology for the meat industry: a review. International Journal of Food Microbiology, 52 , 1999, pp. 1-27.
- 14. L. Lukasse and J.J. Polderdij : Predictive modelling of post-harvest quality evolution in perishables, applied to mushrooms. Journal of Food Engineering, 59, 2003, pp. 191-198.
- 15. H.J. Kim, Y.T. Chang , T.W. Lee, et al. : Optimizing the transportation of international container cargoes in Korea. Maritime Policy & Management, 35, 2008, pp. 103-122.
- 16. Filina-Dawidowicz L. : Rationalization of servicing reefer containers in sea port area with taking into account risk influence. Polish Maritime Research, 21, 2014, pp. 76-85.
- 17. X.Q. Cai, J. Chen , Y.B. Xiao, et al. : Optimization and Coordination of Fresh Product Supply Chains with Freshness-Keeping Effort. Production and Operations Management, 19, 2010, pp. 261-278.
- 18. R .Jedermann, U. Praeger , M. Geyer, et al. : Remote quality monitoring in the banana chain. Philosophical Transactions of the Royal Society a-Mathematical Physical and Engineering Sciences, 372, 2014.
- 19. S.K. Wang : Handbook of Air Conditioning and Refrigeration. Ed.S.K. Wang, 2000.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-eed404e0-62b8-48a7-9ee4-61b7053bcdae