Optimization of Container Handling Systems in Automated Maritime Terminal
Container terminals play a crucial role in global logistic networks. Because of the ever-increasing quantity of cargo, terminal operators need solutions for different decisional problems. In the maritime terminal, at boat arrival or departure, we observe five main problems: the allocation of berths, the allocation of query cranes, the allocation of storage space, the optimization of stacking cranes work load and the scheduling and routing of vehicles. A good cooperation between the different installations in the terminal is important in order to minimize container handling time. In an automated container terminal using Automated Guided Vehicles (AGVs) Query Cranes (QCs) and Automated Stacking Cranes (ASCs) numerical solutions have become essential to optimize operators’ decisions. Many recent researches have discussed the optimization of ACT equipment scheduling using different approaches. In this paper we propose three mathematical models and an exact resolution of QC-AGV-ASC planning, the problem of tasks in an automated container terminal. Our first objective is to minimize the makespan (the time when the last task is achieved). The second objective is to minimize the number of required vehicles.
KeywordsSchedule Problem Container Terminal Automate Guide Vehicle Fleet Size Minimum Cost Flow
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- 1.Meersmans, P.J.M.: Optimization of container handling systems. Ph.D. Thesis, Tinbergen Instutue 271 Erasmus University, Rotterdam (2002)Google Scholar
- 2.Muler, T.: Automated Guided Vehicles. IFS (Publications) Ltd., Springer, UK, Berlin (1983)Google Scholar
- 3.Maxwell, W.L., Muckstadt, J.A.: Design of automatic guided vehicle systems. IIE Transactions 14(2), 114–124 (1982)Google Scholar
- 6.Vis, I.F.A., de Koster, R., Roodbergen, K.J., Peeters, L.W.P.: Determination of the number of automated guided vehicles required at a semi-automated container terminal 52, 409-417 (2001)Google Scholar
- 10.Ahuja, R.K., Magnati, T.L., Orlin, J.B.: Network Flows, Theory, Algorithms, and Appliquations. Prentice Hall, New Jersy (1993)Google Scholar
- 12.Bilge, U., Tanchoco, J.M.A.: AGV Systems with Multi-Load Carriers: Basic Issues and Potential Benefits. Journal of Manufacturing Systems 16 (1997)Google Scholar
- 13.Chen, Y., Leong, T.-Y., Ng, J.W.C., Demir, E.K., Nelson, B.L., Simchi-Levi, D.: Dispatching automated guided vehicles in a mega container terminal. The National University of Singapore/Dept. of IE & MS, Northwestern University (1997)Google Scholar
- 14.Kim, K.H., Bae, J.: A dispatching method for automated guided vehicles to minimize delays of containership operations. International Journal of Management Science 5, 1–25 (1999)Google Scholar
- 15.Bae, M.-K., Park, Y.-M., Kim, K.H.: A dynamic berth scheduling method. Paper Presented at the International Conference on Intelligent Manufacturing and Logistics Systems (IML 2007), Kitakyushu, Japan, February 26–28 (2007)Google Scholar
- 16.Dahlstrom, K.J., Maskin, T.: Where to use AGV systems, manual fork lifts, traditional fixed roller conveyor systems respectively. In: Proceedings of the 1st International AGV Conference, 173–182 (1981)Google Scholar
- 17.Muller, T.: Comparaison of operating costs between different transportation systems. In: Proceedings of the 1st International AGV Conference, pp. 145–155Google Scholar
- 18.Sinirech, D., Tanchoco, J.M.A.: AN economic model for determining AGV fleet size. International Journal of Production Research 29(9), 1725–1268 (1992)Google Scholar