HMS2006_F1 - Liophant Simulation

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Usage percentage Current situation Scenario 1 Scenario 2 Gantry crane 47% * 74% ±1% 87% ±2% Reachstacker 1 63% ±5% 84% ±4% 89% ±3% Reachstacker 2 63% ±6% 83% ±4% 89% ±3% Labour 1 66% ±5% 86% ±3% 87% ±6% Labour 2 59% ±6% 82% ±4% 85% ±2% Labour 3 47% 75% ±1% 85% ±2% Tracks 100% 100% 100% Table 1. Efficiency indicators Indicator Current situation Scenario 1 Scenario 2 Avg. num. of 40-feet cont. in the storage area 529 ±11 602 ±12 629 ±33 Avg, waiting time in the storage area (min.) 5610 ±30 5040 ±10 3600 ±66 Unattended trains 0 0 7 Avg time in the storage area (including urgent ones) (min.) 5300 ±30 4800 ±10 3600 ±57 Table 2. Effectiveness indicators As an overall conclusion with regard to the computational results, the dry port is expected to meet the demand both in the short and the medium term. Were there more containers available the system could offer a higher level of quality service, although this decision is beyond the dry port managers’ responsibility. Besides, in the current operating conditions and working on a 24 hour/day basis, if the demand reached its maximum potential value, there would be some shortage of resources and some trains would not be attended. In case the managers desired to overcome this ineffectiveness operating as the dry port does now, it would be necessary to acquire some additional assets, to the detriment of the efficiency (since a higher lever of idleness would be expected). 6. Conclusions An example of a simulation study to carry on a quantitative analysis of the performance of a intermodal terminal of a particular nature (a dry port) has been presented. The same methodology applied to similar studies has been successfully used, introducing the appropriate modifications to represent the particularities of the system at study. Besides some improvements have been included, being the variability of the train length (between a lower and an upper bound) and the more accurate representation of the loading/unloading processes by means of using a gantry crane. The model, verified and validated, has allowed the prediction of its performance in two plausible scenarios, and asses this performance in terms of quality service and resource utilisation. Acknowledgements The authors would like to thank the dry port managers for their attention and their time, which provided with accurate and valuable data, facilitating the success of the study. References • Gambardella, L. M. y Rizzoli, A. E. (2000). The role of simulation and optimisation in intermodal container terminals. Proceedings of ESS2000 12 th European Simulation Symposium and Exhibition, pp. 422-6.
• García, I. y Gutiérrez, G. (2003). Simulation model for strategic planning in rail freight transport systems. Institute of Transportation Engineers, Vol. 73, No. 9, pp. 32-40. • Kulick, B. C. y Sawyer, J. T. (2001). The use of simulation to calculate the labor requirements in an intermodal rail terminal. Proceedings of the 2001 Winter Simulation Conference, pp. 1038-41. • Rizzoli, A. E., Fornara, N. y Gambardella, L. M. (2002). A simulation tool for combined rail/road transport in intermodal terminals. Mathematics and Computer Simulation, No. 59, pp. 57-71. • Vis, I.F.A. y Koster R. de (2003). Transshipment of containers at a container terminal: An overview. European Journal of Operational Research, Vol. 147, pp. 1- 16.
Page 1 and 2: Capacity Assessment Via Simulation
Page 3 and 4: Figure 1. Areal sight of the dry po
Page 5: Once the model had been programmed

HMS2006_F1 - Liophant Simulation

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