Using simulation to optimize transhipment systems: Applications in field

This article presents a new simulation-based meta-model to support logistics providers in sizing transhipment systems. The meta-model is made up of: a user interface with database (where system elements and their parameters are specified); a library of objects (which represent all the elements of the system) and a software application to automatically build the simulation model of the system. The proposed meta-model – through a unique tool – allows service providers to design transhipment systems for various environments, whenever the experience from previous projects is often helpless. The suggested approach has been successfully used in two case-studies: an effective cost analysis has been carried out, by taking into account the expected effects on both revenues and investments; and the robustness of each system’s configuration has been tested by analysing its performance under different levels of various sources of variance.

[1]  Marielle Christiansen,et al.  Industrial aspects and literature survey: Fleet composition and routing , 2010, Comput. Oper. Res..

[2]  Operations research methods in maritime transport and freight logistics , 2009 .

[3]  S. P. Dutta,et al.  Determination of the optimal number of kanbans and kanban allocation in a FMS: A simulationbased study , 1999 .

[4]  Kripa Shanker,et al.  DETERMINATION OF OPTIMAL AGV FLEET SIZE FOR AN FMS , 1998 .

[5]  Pandu R. Tadikamalla,et al.  Multicriteria design and control of a cellular manufacturing system through simulation and optimization , 1998 .

[6]  Dimitrios V. Lyridis,et al.  Liner shipping cycle cost modelling, fleet deployment optimization and what-if analysis , 2011 .

[7]  Nikolay Tchernev,et al.  Object-oriented methodology for FMS modelling and simulation , 1997 .

[8]  Jing Lu,et al.  Research of import iron ore logistics system based on the minimum cost theory , 2005, Proceedings of ICSSSM '05. 2005 International Conference on Services Systems and Services Management, 2005..

[9]  Koos Frouws,et al.  Simulation-based optimization of ship design for dry bulk vessels , 2011 .

[10]  H. Sherali,et al.  A coal shipping and blending problem for an electric utility company , 2000 .

[11]  Hyunbo Cho,et al.  Determination of efficient simulation model fidelity for flexible manufacturing systems , 2005, Int. J. Comput. Integr. Manuf..

[12]  Kripa Shanker,et al.  An heuristic for configuring a mixed uni/bidirectional flow path for an AGV system , 1998 .

[13]  Kjetil Fagerholt,et al.  Ship Routing and Scheduling: Status and Perspectives , 2004, Transp. Sci..

[14]  Z. M. Wu.  Modelling and simulation of an intelligent flexible manufacturing system via high-level object Petri nets (HLOPN) , 2005 .

[15]  L. Shih.  Planning of fuel coal imports using a mixed integer programming method , 1997 .

[16]  Georgios K. D. Saharidis,et al.  The berth allocation problem: Optimizing vessel arrival time , 2009 .