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Dynamic Decision Modeling for Inland Waterway Disruptions: August 1, 2014 – December 31, 2016: Final Research Report
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Dynamic Decision Modeling for Inland Waterway Disruptions: August 1, 2014 – December 31, 2016: Final Research Report
  • Alternative Title:
    Dynamic decision modeling for inland waterway disruptions : August 1, 2014 ¿ December 31, 2016.
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    Final Research Report
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  • Abstract:
    Inland waterway system is an essential part of the U.S. transportation system, which provides efficient and economic freight transportation through 12,000 miles of navigable waterway. An unexpected event (such as a natural event or unscheduled maintenance) may block a section on the inland waterway and lead to disruption of the transportation. Considering the broad economic and societal impacts caused by the disruption, closures of the inland waterway system can become a significant problem for the shippers and other stakeholders involved in barge transportation. When the disruption occurs, there exist two available actions: (1) waiting at the current location until the locked traffic to clear, and (2) rerouting using alternative transportation modes. However, the uncertainty inherent in disruptions makes the decision making process complicated. In this research, we construct a framework for decision making under uncertainty when disruption on the inland waterway occurs. A comprehensive transportation cost model is developed which reflects objectives and goals of the stakeholders who are directly or indirectly involved with freight transportation. In addition, the uncertainty of the closure is studied in terms of the expected closure duration affecting the transportation cost. A supporting spreadsheet based tool is devised to facilitate real-time decision making. The numerical experiment reveals the significance of the private indirect cost in decision making and the existence of a threshold type policy. The investigation concerning historical data of the closure events shows the significant relationship between the weather condition and the duration of the disruption. Having a more accurate estimate of the disruption duration will improve the decision making process.
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