Login | Register

Investigating congestion mitigation scenarios to reduce truck turn time at Port of Montreal using Discrete Event Simulation

Title:

Investigating congestion mitigation scenarios to reduce truck turn time at Port of Montreal using Discrete Event Simulation

Alagesan, Vignesh (2017) Investigating congestion mitigation scenarios to reduce truck turn time at Port of Montreal using Discrete Event Simulation. Masters thesis, Concordia University.

[img]
Text (application/pdf)
Alagesan_MASc_S2018.pdf - Accepted Version
Restricted to Repository staff only until 1 June 2020.
Available under License Spectrum Terms of Access.
5MB

Abstract

Container ports are facing the growing problem of congestion due to the high volume of container trucks entering the terminal. Globalization, growth of trade and increasing consumer demand have further added to this complexity which has resulted in increased greenhouse gas emissions at the ports. Several measures are being undertaken by the ports to reduce this problem and improve port sustainability. Examples of these measures are implementing advanced technology equipment, implementing extended gate hours, changing the arrival patterns of trucks, and implementing variable gate lane policies.
The objective of the thesis is to develop a discrete event simulation (DES) model to investigate the congestion mitigation scenarios to improve terminal productivity and reduce truck turn times at the Port of Montreal. A case study with the Montreal Port Authority is conducted. The results of our simulation study yield upgrade of technology at the terminals as the best solution followed by managing the arrival patterns, changing gate lanes and extended gating hours. The proposed work is novel and one of the very few to be conducted in the context of Port of Montreal. The generated results can be used by decision makers at Port of Montreal in developing strategies to mitigate congestion and reduce truck turn times at terminals.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical and Industrial Engineering
Item Type:Thesis (Masters)
Authors:Alagesan, Vignesh
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Industrial Engineering
Date:23 November 2017
Thesis Supervisor(s):Awasthi, Anjali
Keywords:Discrete event simulation, Scenario analysis, Technology, Greenhouse gas emissions, Green harbor trucking, Congestion Mitigation.
ID Code:983258
Deposited By: Vignesh Alagesan
Deposited On:11 Jun 2018 03:03
Last Modified:11 Jun 2018 03:03

References:

Arango, C., Cortes, P., Munuzuri, J. and Onieva, L. (2011). Berth Allocation Planning in Seville Inland Port by Simulation and Optimization. Advanced Engineering Informatics, 25(3): 452–461.
Asperen E., Borgman B. and Dekker R. (2011). Evaluating impact of truck announcements on container stacking efficiency, Flexible Services, and Manufacturing Journal, 25:543–556
Azab, Ahmed, and Eltawil, Amr. (2016). A Simulation Based Study of the Effect of Truck Arrival Patterns on Truck Turn Time in Container Terminals. 10.7148/2016-0080. Bruzzone A.G., Mosca R., Revetria R., Rapallo S., (2000). Risk analysis in harbor environments using Simulation, Safety Science, 35, 75–86 Bruzzone, A., andSignorile, R., (1998). Simulation and genetic algorithms for ship planning and shipyard layout. Simulation 71: 74–83.
Chen G., Govindan K. and Yang Z. (2013). Managing truck arrivals with time windows to alleviate gate congestion at container terminals. Int. J. Production Economics, 141 (2013) 179–188
Chen G., Govindan K., Yang Z.and Choi T. and Jiang L. (2013). Terminal appointment system design by nonstationary M(t)/Ek/c(t) queueing model and genetic algorithm, Int. J. Production Economics, 146 (2013)694– 703 Chen, Xiaoming; Zhou, Xuesong; Listand George F. (2011). Using time-varying tolls to optimize truck arrivals at ports. Transportation Research Part E: Logistics and Transportation Review, 47(6), 965-982.
111
Daganzo, C. F., (1994). The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research Part B: Methodological, 28(4), 269-287. Denning, P.J., and Buzen, J. P. (1978). The operational analysis of queueing network models. ACM Computing Surveys (CSUR), 10(3), 225-261. Duinkerken, M. B., Ottjes, J. A., Evers, J. J. M., Kurstjens, S., Dekker, R. and Dellaert, N. (1996). Simulation studies on inter terminal transport at the Maasvlakte. In: Proceedings 2nd TRAIL PhD Congress 1996 ‘Defence or attack’, May 1996, Rotterdam [TRAIL], http://www.ocp.tudelft.nl/tt/users/duinkerk/papers/rot9605a.pdf – last check of address: July 30, 2003. Edmond, E., and Maggs, R., (1978). How useful are queue models in port investment decisions for container berths. Journal of the Operational Research Society, 29(8), 741-750. Floyd, S., and Jacobson, V. (1993). Random Early Detection Gateways for Congestion Avoidance. IEEE-ACM Transactions on Networking, 1(4), 397-413.
Gambardella, L. M., Rizzoli, A. E. and Zaffalon, M., (1998). Simulation and planning of an intermodal container terminal. Simulation, 71(2), 107–116.
Gambardella, L.M., and Rizzoli, A. E. (2000). The role of simulation and optimization in intermodal container terminals, 12th European Simulation Symposium and Exhibition (ESS 2000), Hamburg, Germany, The Society of Computer Simulation International 2000.
Giuliano, G., and I. O'Brien. (2007). Reducing Port-Related Truck Emissions: The Terminal Gate Appointment System at the Ports of Los Angeles and Long Beach. Transportation Research Part D 12(7), 460-473.
112
Guan C. and Liu R. (2009). Container terminal gate appointment system optimization. Maritime Economics and Logistics 11, 378–398. doi:10.1057/mel.2009.13 Hayuth, Y., Pollatschek, M., and Roll, Y.(1994). Building a port simulator. Simulation, 63(3), 179-189.
Huynh N. and Walton C. (2011). Improving Efficiency of Drayage Operations at Seaport Container Terminals Through the Use of an Appointment System. Handbook of Terminal Planning Volume 49 of the series Operations Research/ Computer Science Interfaces Series 323-344.
Huynh, N., Hutson, N., (2005). Mining the sources of delay for dray trucks at container terminals. Transportation Research Record No. 2066, 41–49
Huynh, N., Walton, C. M., (2008). Robust scheduling of truck arrivals at marine container terminals. Journal of Transportation Engineering 134(8), 347–353.
John Liu, Hong Yan, Jie Wu. (2010). DEA models for identifying sensitive performance measures in container port evaluation, Maritime Economics and Logistics. 12: 215
Kiani Moghadam, Mansoor and Sayareh, Jafar and Noor Amin, Saeed. (2010). A Simulation Framework for Optimising Truck Congestions in Marine Terminals. Journal of Maritime Research. Vol. VII. 55-70.
Kozan, E. (1997). Comparison of analytical and simulation planning models of seaport container terminals. Transportation Planning and Technology, 20(3), 235-248.
Lee, T.-W., Park, N.-K. And Lee, D.-W., (2003). A simulation study for the logistics planning of a container terminal in view of scm. Maritime Policy and Management, 30(3), 243–254.
113
Legato, P. and Mazza, R. M., (2001). Berth planning and resources optimization at a container terminal via discrete event simulation. European Journal of Operational Research, 133(3), 537–547.
Lei Hu (2011). Application of RFID Technology at the Entrance Gate of Container Terminals, ICCL 2011, LNCS 697.
Liu, C. I., Jula, H. and Ioannou, P. A., (2002). Design, simulation, and evaluation of automated container terminals. IEEE Transactions on Intelligent Transportation Systems, 3(1), 12–26.
Mathew, R, and Mastaglio, Thomas and Lewis, A. (2012). A simulation tool for high-fidelity modeling of complex logistical networks. 24th European Modeling and Simulation Symposium, EMSS 2012. 6-14.
Monaldo Mastrolilli et al (1998). Resource Allocation and Scheduling of Operations in an Intermodal Terminal. 10th European Simulation Symposium and Exhibition, Simulation in Industry.
Moon, K. (2000). A mathematical model and a heuristic algorithm for berth planning. Brain Korea, Vol. 21. Morais, P. and Lord, E. (2006). Terminal appointment system study, Prepared for Transportation Development Centre of Transport Canada, http://www.tc.gc.ca/media/documents/policy/14570e.pdf, accessed July 2013.
Mosca, R., Giribone, P., and Bruzzone, A. (1994). Simulation and Automatic Parking in a Training System for Terminal Container Yard Management. Proceedings of ITEC94, The Hague, April, 26-28
Murty, K.G., Liu, J., Wan, Y., andLinn, R. (2005). A decision support system for operations in a container terminal. Decision Support Systems, 39(3), 309-332.
114
Ottjes, J. A., (2006). Simulation of a multiterminal system for container handling. OR Spectrum, 28(4), 447.
Parola, F. and Sciomachen, A. (2005). Intermodal Container Flows in a Port System Network: Analysis of Possible Growths via Simulation Models. International Journal of Production Economics. Elsevier, 97, 75-88.
Peng-Hong Koh, Jimmy L.K. Goh, Hak-Soon Ng, Hwei-Chiat Ng. (1994). Using Simulation to Preview Plans of a Container Port Operations. Proceedings of the 1994 Winter Simulation, Conference.
Saanen, Y. (2005). Using emulation to improve the performance of your TOS. In: Automation in Port Operations, September 26–27, Antwerp. Saanen, Y. A. (2000). Examining the potential for adapting simulation software to enable short-term tactical decision making for operational optimization. Technical Report, TBA Nederland/Delft University of Technology. Saanen, Y. A. (2002). The application of advanced simulations for the engineering of logistic control systems. Building blocks for Effective Telematics Application Development and Evaluation, 173.
Sgouridis, S. P., Makris, D. and Angelides, D. C., (2003). Simulation analysis for midterm yard planning in container terminal. Journal of Waterway, Port, Coastal, and Ocean Engineering, 129(4), 178–187.
Shabayek, A. A., and Yeung, W. W., (2002). A simulation model for the Kwai Chung container terminals in Hong Kong. European Journal of Operational Research, 140(1), 1–11.
115
Sharif, Omor and Huynh, Nathan and M. Vidal, Jose. (2011). Application of El Farol model for managing marine terminal gate congestion. Research in Transportation Economics. 32. 81-89. 10.1016/j.retrec.2011.06.004.
Soriguera, F., Espinet, D. and Robuste, F., (2006). A simulation model for straddle carrier operational assesment in a marine container terminal. Journal of Maritime Research, 3(2), 19–34.
Soriguera, F., Robusté, F., Juanola, R., and López Pita, A. (2006). Handling equipment optimization in container terminal of Port of Barcelona. Spain. Transportation Research Record, 1963, TRB, 44-51
Spasovic, L. N., Dimitrijevic, B., and Rowinski, J. (2009). Extended Hours of Operation at the Port Facilities in New Jersey: A Feasibility Analysis. Newark: New Jersey Institute of Technology.
Stahlbock, R., and VOß, S. (2008). Operations research at container terminals: a literature update. Or Spectrum, 30(1), 1-52.
Steenken, D., VOß, S., andStahlbock, R. (2004). Container terminal operation and operations research-a classification and literature review. OR Spectrum, 26(1), 3-49.
Thiers, G.F., andJanssens, G.K. (1998). A port simulation model as a permanent decision instrument. Simulation, 71(2), 117-125. Tsilingris, P. S., Psaraftis, H. N. and Lyridis, D. V. (2007). Radio frequency identification (RFID) technology in ocean container transport, International Association of Maritime Economists Conference, July 4-6, Athens, Greece.
Van Woensel, T., and Vandaele, N. (2007). Modeling traffic flows with queueing models: a review. Asia-Pacific Journal of Operational Research, 24(04), 435-461.
116
Wolfe, M. and Troup, K. (2005). The freight technology story: Intelligent freight technologies and their benefits, Report FHWA-HOP-05-030 for the US Department of Transportation, http://ops.fhwa.dot.gov/freight/intermodal/freight_tech_story/freight_tech_story.htm, accessed July 2013.
Yang, C., Choi, Y. and Ha, T., (2004). Simulation-based performance evaluation of transport vehicles at automated container terminals. OR Spectrum, 26, 149–170.
Yun, W. Y. and Choi, Y. S., (1999). A simulation model for container-terminal operation analysis using an object-oriented approach. International Journal of Production Economics, 59(1–3), 221–230.
Zehendner E. and Feillet D. (2013). Benefits of a truck appointment system on the service quality of inland transport modes at a multimodal container terminal. European Journal of Operational Research 235 (2014) 461–469.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top