Ahmed, Essmeil (2015) Optimization-Based Simulation of Container Terminal Productivity using Yard Truck Double Cycling. PhD thesis, Concordia University.
Preview |
Text (application/pdf)
9MBAhmed_PhD_F2015.pdf - Accepted Version |
Abstract
ABSTRACT
The growth of global trade transiting over the ocean has been continually increasing. A new generation of large vessels has recently been introduced to the transhipment system. These large vessels can carry more than 16000 twenty-foot equivalent container units (TEUs), maximizing shipping productivity. Container terminals must improve their productivity to meet the rapid increases in trade demand and to keep pace with developments in the shipbuilding industry. Reducing vessel turnaround time in container terminals increases the capacity for world trade. This time reduction can be achieved by improving one or more container terminal major resources or factors.
The objective of this research is to maximize container terminal productivity by minimizing vessel turnaround time within reasonable hourly and unit costs. A new strategy is introduced, employing double cycling to reduce the empty travel of yard trucks. This double-cycling strategy still requires the use a single-cycle strategy before the trucks can be incorporated into double-cycle scheduling. The single-cycle start-up is necessary in order to create enough space to begin loading a vessel if there is no other space.
The strategy is based on combining the efforts of two quay cranes (Unloading and Loading quay cranes) to work as a unit. The technique optimizes the number of trucks in terms of time and cost, minimizing yard truck cycles by minimizing single cycle routes and maximizing double cycle trips. This requires five steps. First, a good knowledge base of a container terminal’s operation and of the behaviours of the Quay cranes (QCs), Yard trucks, and Yard cranes needs to be constructed. Second, analysis of the collected data is required to simulate the container terminal operation and to implement the Genetic algorithm. Third, the double cycling truck strategy is simulated, tested and verified. Fourth, sensitivity analysis is performed to rank and select the best alternatives. Optimization of the selected alternatives in terms of productivity and cost as well as verifying the results using real case studies comprises the fifth step.
Genetic Algorithm is used to optimize the results. Some selection approaches are implemented on the set of the nearest optimum solutions to rank and select the best alternative. The research offers immediate value by improving container terminal productivity using existing facilities and resources. Simulating the yard truck double cycling strategy provides container terminal mangers and decision makers with a clear overview of their handling container operations. Optimizing fleet size is a key factor in minimizing container handling costs and time. The simulation model reveals a productivity improvement of about 19% per QC. A reasonable cost savings in terms of the cost index in unit cost was achieved using yard truck double cycling operation. The genetic algorithm corroborates the achievements thus gained and determines the optimal fleet size that will result in the maximum terminal productivity (quickest vessel turnaround time) with the minimal cost. A time reduction of more than 26% was achieved in most cases, compared to previous research efforts.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering |
|---|---|
| Item Type: | Thesis (PhD) |
| Authors: | Ahmed, Essmeil |
| Institution: | Concordia University |
| Degree Name: | Ph. D. |
| Program: | Building Engineering |
| Date: | 3 June 2015 |
| Thesis Supervisor(s): | Zayed, Tarek and Alkass, Sabah |
| ID Code: | 980150 |
| Deposited By: | ESSMEIL AHMED |
| Deposited On: | 27 Oct 2015 19:27 |
| Last Modified: | 18 Jan 2018 17:50 |
References:
ReferencesAhmed, E., Zayed, T. & Alkass, S., 2014a. Improving productivity of yard trucks in port container terminal using computer simulation. 31st International Symposium on Automation and Robotics in Construction and Mining, ISARC 2014 - Proceedings, 278 – 285.
Ahmed, E., Zayed, T. & Alkass, S., 2014b. Simulation model of yard truck double cycling to improve container terminal productivity. 16th International Conference on Harbor, Maritime and Multimodal Logistics Modelling and Simulation, HMS 2014, 1 – 9.
Aykagan, 2008. Berth and Quay Crane Scheduling : Problems , Models and Solution Methods. Georgia Institute of Technology.
Bartošek, A. & Marek, O., 2013. Quay Cranes in Container Terminals. Transactions on Transport Sciences, 6(1).
Bazzazi, M., Safaei, N. & Javadian, N., 2009. A genetic algorithm to solve the storage space allocation problem in a container terminal. Computers and Industrial Engineering, 56(1), 44–52.
Beškovnik, B. & Twrdy, E., 2010. Planning organization and productivity simulation tool for maritime container terminals. Transport, 25(3), 293–299.
Bierwirth, C. & Meisel, F., 2010. A survey of berth allocation and quay crane scheduling problems in container terminals. European Journal of Operational Research, 202(3), 615–627.
Carlo, H.J., Vis, I.F.A. & Roodbergen, K.J., 2014a. Storage yard operations in container terminals: Literature overview, trends, and research directions. European Journal of Operational Research, 235(2), 412–430.
Carlo, H.J., Vis, I.F.A. & Roodbergen, K.J., 2014b. Transport operations in container terminals: Literature overview, trends, research directions and classification scheme. European Journal of Operational Research, 236(1), 1–13.
Cheong, C.Y. et al., 2010. Multi-objective optimization of large scale berth allocation and quay crane assignment problems. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 669–676.
Cordeau, J-F. Legato, P, Mazza, R. & Trunfio R., 2015. Simulation-based optimization for housekeepingin acontainer transshipment terminal. Computers &Operations Research (53), 81–95.
Daganzo, C.F., 1989. The cranescheduling problem. Transportation Research Part B Methodological., 23 B(3), 159–175.
Duinkerken, M.B., Evers, J.J.M. & Ottjes, J. a, 2001. A simulation model for integrating quay transport and stacking policies on automated container terminals. Modelling and Simulation 2001, (June), 909–916.
Goodchild, a. V. & Daganzo, C.F., 2007. Crane double cycling in container ports: Planning methods and evaluation. Transportation Research Part B: Methodological, 41, 875–891.
Goodchild, A. & Daganzo, C., 2005. Crane double cycling in container ports: affect on ship dwell time, California, Berkeley.
Goodchild, A. V., 2005. Crane Double Cycling in Ports: Algorithms, Evaluation, and Planing. University of California, Berkeley.
Goodchild, A. V. & Daganzo, C.F., 2006. Double-Cycling Strategies for Container Ships and Their Effect on Ship Loading and Unloading Operations. Transportation Science, 40(4), 473–483.
Guenther, H.-O., Grunow, M. & Lehmann, M., 2005. AGV dispatching strategies at automated seaport container terminals. Operations Research and Its Applications, 5, 48–64.
Haupt, R.L., Haupt, S.E. & Wiley, a J., 2004. Algorithms Practical Genetic Algorithms,
Hwang, C.-L., Lai, Y.-J. & Liu, T.-Y., 1993. A new approach for multiple objective decision making. Computers & Operations Research, 20(8), 889–899.
Iason, Gk., 2014. Infrastructure Performance Assessment of Subway Networks. Concordia University , Montreal, Canada.
Imai, A., Chen, H.C., et al., 2008. The simultaneous berth and quay crane allocation problem. Transportation Research Part E: Logistics and Transportation Review, 44, 900–920.
Imai, A., Nishimura, E. & Papadimitriou, S., 2008. Berthing ships at a multi-user container terminal with a limited quay capacity. Transportation Research Part E: Logistics and Transportation Review, 44, 136–151.
Imai, A., Nishimura, E. & Papadimitriou, S., 2013. Marine container terminal configurations for efficient handling of mega-containerships. Transportation Research Part E: Logistics and Transportation Review, 49(1), 141–158.
Jordan, M., 2002. Quay crane productivity. In Terminal Operation Conference Americas, Miami, FL, USA. Miami, FL.
Katalinic, E.B., 2011. Selection of as / rss by using fuzzy topsis method.Annals of DAAAM for 2011 & Proceedings of the 22nd International DAAAM Symposiu, 22(1), 1427–1429.
Lane, A., Charles M., 2014. The impact of ever larger vessels on terminals, Mega Ship Ready, Edition 64-November 2014, 18-20
Lee, D.-H. & Wang, H.Q., 2010. An approximation algorithm for quay crane scheduling with handling priority in port container terminals.
Lee, D.H., Wang, H.Q. & Miao, L., 2008. Quay crane scheduling with non-interference constraints in port container terminals. Transportation Research Part E: Logistics and Transportation Review, 44, 124–135.
Lee, L.H. et al., 2007. An optimization model for storage yard management in transshipment hubs. Container Terminals and Cargo Systems: Design, Operations Management, and Logistics Control Issues, 561, 107–129.
Lee, T.-W., Park, N.-K. & Lee, D.-W., 2003. A simulation study for the logistics planning of a container terminal in view of SCM. Maritime Policy & Management, 30(January 2015), 243–254.
Liang, C., Huang, Y. & Yang, Y., 2009. A quay crane dynamic scheduling problem by hybrid evolutionary algorithm for berth allocation planning. Computers and Industrial Engineering, 56(3), 1021–1028.
Linn, R. et al., 2003. Rubber tired gantry crane deployment for container yard operation. Computers and Industrial Engineering, 45, 429–442.
Liu, C.I. et al., 2004. Automated guided vehicle system for two container yard layouts. Transportation Research Part C: Emerging Technologies, 12, 349–368.
Liu, C.-I., Jula, H. & Ioannou, P.A., 2002. Design, simulation, and evaluation of automated container terminals. IEEE Transactions on Intelligent Transportation Systems, 3(1), 12–26.
Meisel, F. & Wichmann, M., 2010. Container sequencing for quay cranes with internal reshuffles. OR Spectrum, 32, 569–591.
Moccia, L. et al., 2006. A branch-and-cut algorithm for the quay crane scheduling problem in a container terminal. Naval Research Logistics, 53, 45–59.
Murty, K., 2007. Yard crane pools and optimum layouts for storage yards of container terminals. Journal of Industrial and Systems Engineering.
Murty, K.G., 2007. Yard Crane Pools and Optimum Layouts for Storage Yards of Container Terminals. Nature, 1(3), 190–199.
Nam, K.-C., Kwak, K.-S. & Yu, M.-S., 2002. Simulation Study of Container Terminal Performance. Journal of Waterway, Port, Coastal, and Ocean Engineering, 128(June), 126–132.
Nguyen, V.D. & Kim, K.-H., 2010. Minimizing Empty Trips of Yard Trucks in Container Terminals by Dual Cycle Operations. Industrial Engineering and Management Systems, 9(1), 28–40.
Pap, E. et al., 2011. Optimization of container quay cranes operations. In SISY 2011 - IEE 9th International Symposium on Intelligent Systems and Informatics, Proceedings. 137–140.
Park, Y.-M. & Kim, K.H., 2003. A scheduling method for Berth and Quay cranes. OR Spectrum, 25(1), 1–23.
Petering, M. & Murty, K., 2006. Simulation analysis of algorithms for container storage and yard crane scheduling at a container terminal. Proceedings of the Second International Intelligent Logistic Systems Confrence.
Petering, M.E.H. & Murty, K.G., 2009. Effect of block length and yard crane deployment systems on overall performance at a seaport container transshipment terminal. Computers and Operations Research, 36, 1711–1725.
Roshandel, J., Miri-Nargesi, S.S. & Hatami-Shirkouhi, L., 2013. Evaluating and selecting the supplier in detergent production industry using hierarchical fuzzy TOPSIS. Applied Mathematical Modelling, 37(24), 10170–10181.
S. M. Homayouni, 2011. Using simulated annealing algorithm for optimization of quay cranes and automated guided vehicles scheduling. African Journal of Business Management, 6, 550–555.
Sammarra, M. et al., 2007. A tabu search heuristic for the quay crane scheduling problem. Journal of Scheduling, 10, 327–336.
Savić, D. a., Bicik, J. & Morley, M.S., 2011. A DSS generator for multiobjective optimisation of spreadsheet-based models. Environmental Modelling and Software, 26(5), pp.551–561.
Steenken, D., Voss, S. & Stahlbock, R., 2004. Container terminal operation and operations research-a classification and literature review. OR Spectrum, 26, 3–49.
Tavakkoli-Moghaddam, R. et al., 2009. An efficient algorithm for solving a new mathematical model for a quay crane scheduling problem in container ports. Computers and Industrial Engineering, 56(1), 241–248.
Vis, I.F. a & De Koster, R., 2003. Transshipment of containers at a container terminal: An overview. European Journal of Operational Research, 147, 1–16.
Wang, Z. X. Chan Felix, T. S. Chung, S. H & Niu, B. , 2015. Minimization of Delay and Travel Time of Yard Trucks in Container Terminals Using an Improved GA with Guidance Search. Mathematical Problems in Engineering, 2015, 1-12
Yap, W.Y. & Lam, J.S.L., 2013. 80 million-twenty-foot-equivalent-unit container port? Sustainability issues in port and coastal development. Ocean and Coastal Management, 71, 13–25.
Yong, D., 2006. Plant location selection based on fuzzy TOPSIS. International Journal of Advanced Manufacturing Technology, 28, 839–844.
Zayed, T. & Halpin, D., 2001. Simulation of concrete batch plant production. Journal of Construction Engineering and Management.
Zhang, C. et al., 2003. Storage space allocation in container terminals. Transportation Research Part B: Methodological, 37, 883–903.
Zhang, H. & Kim, K., 2009. Maximizing the number of dual-cycle operations of quay cranes in container terminals. Computers & Industrial Engineering, (56), 979–992.
Zhang, J.L. & Qi, X.W., 2012. Induced interval-valued intuitionistic fuzzy hybrid aggregation operators with TOPSIS order-inducing variables. Journal of Applied Mathematics, 2012.
Zhang, R. Zhihong, J Ma, Y. & Luan, W., 2015. Optimization for two-stage double-cycle operattions in container terminals. Computer & Industrial Engineering (83), 316–326.
Repository Staff Only: item control page
Download Statistics
Download Statistics