Prediction of Arrival Times and Human Resources Allocation for Container Terminal

Increasing competition in the container shipping sector has meant that terminals are having to equip themselves with increasingly accurate analytical and governance tools. A transhipment terminal is an extremely complex system in terms of both organization and management. Added to the uncertainty surrounding ships’ arrival time in port and the costs resulting from over-underestimation of resources is the large number of constraints and variables involved in port activities. Predicting ships delays in advance means that the relative demand for each shift can be determined with greater accuracy, and the basic resources then allocated to satisfy that demand. To this end, this article proposes 2 algorithms: a dynamic learning predictive algorithm based on neural networks and an optimization algorithm for resource allocation. The use of these 2 algorithms permits on the one hand to reduce the uncertainty interval surrounding ships’ arrival in port, ensuring that human resources can be planned around just 2 shifts. On the other hand, operators can be optimally allocated for the entire workday, taking into account actual demand and operations of the terminal. Moreover, as these algorithms are based on general variables they can be applied to any transhipment terminal. Future integration of the 2 models within a broader decision support system will provide an important support tool for planners for fast, flexible planning of the terminal's operations management.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13881973
• Authors:
  • Fancello, G
  • Pani, Claudia
  • Pisano, Marco
  • Serra, Patrizia
  • Zuddas, Paola
  • Fadda, P
• Publication Date: 2011-6

Language

• English

Media Info

• Media Type: Print
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 142-173
• Serial:
  • Maritime Economics & Logistics
  • Volume: 13
  • Issue Number: 2
  • Publisher: Palgrave Macmillan
  • ISSN: 1388-1973
  • EISSN: 1476-0592
  • Serial URL: http://link.springer.com/journal/41278

Subject/Index Terms

• TRT Terms: Container terminals; Decision support systems; Forecasting; Logistics; Neural networks; Ocean shipping; Optimization; Resource allocation
• Subject Areas: Marine Transportation; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01342206
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 21 2011 9:28AM