Simulation at a maritime container terminal: Models and computational frameworks

NEC-Italy high-performance computing division recognises that computer simulation aided organisation and management is the true challenge for a new generation of advanced decision tools for supporting operations in modern logistic platforms. Collecting academic expertises, research skills, computing machinery and dynamic realities with a significant growth into the field of container terminal logistics is a new R&D project at the CESIC-NEC center at the University of Calabria. The starting idea of this project is that discrete-event simulation is the best modelling approach to manage the complexity of logistic processes at container terminals. For these time-based systems, operating in a stochastic environment, becomes crucial to highlight both congestion and starvation phenomena embedded into logistic processes, in order to achieve reasonable targets of a good management of resources and, therefore, stay on the market. Here we present some queuing network based representations that are at the basis of an integrated simulation model under development. Since large and complex models are affected by a high burden on execution, we also remark the benefits of parallel and/or distributed computational frameworks. Numerical results on parallel analysis of simulation output data are given.

[1]  Iris F. A. Vis,et al.  Transshipment of containers at a container terminal: An overview , 2003, Eur. J. Oper. Res..

[2]  Andrzej Bargiela,et al.  A model of granular data: a design problem with the Tchebyschev FCM , 2005, Soft Comput..

[3]  Roberto Musmanno,et al.  A queuing network model for the management of berth crane operations , 2008, Comput. Oper. Res..

[4]  R. Asariotis,et al.  Review of Maritime Transport, 2008 , 2008 .

[5]  J. Stiglitz,et al.  United Nations Conference Ontrade and Development , 2005, International Organizations and the Law of the Sea 2002.

[6]  Agostino G. Bruzzone,et al.  Distributed simulation and industry: potentials and pitfalls , 2002, Proceedings of the Winter Simulation Conference.

[7]  Constantine Lazos,et al.  Steady state simulations on queuing processes in parallel time streams: Problems and potentialities , 2001, HERCMA.

[8]  Gilbert Laporte,et al.  Models and Tabu Search Heuristics for the Berth-Allocation Problem , 2005, Transp. Sci..

[9]  Beatrice Gralton,et al.  Washington DC - USA , 2008 .

[10]  Andrzej Bargiela,et al.  Granular prototyping in fuzzy clustering , 2004, IEEE Transactions on Fuzzy Systems.

[11]  Pasquale Legato,et al.  An Integrated Simulation Model For Channel Contention And Berth Management At A Maritime Container Terminal , 2007 .

[12]  Andrzej Bargiela,et al.  Fuzzy fractal dimensions and fuzzy modeling , 2003, Inf. Sci..

[13]  Averill M. Law,et al.  Simulation modelling and analysis , 1991 .

[14]  R. E. Nance,et al.  A comparison of selected conceptual frameworks for simulation modeling , 1989, WSC '89.

[15]  Pasquale Legato,et al.  Simulation-based optimization for the quay crane scheduling problem , 2008, 2008 Winter Simulation Conference.

[16]  Demetrio Laganà,et al.  Solving simulation optimization problems on grid computing systems , 2006, Parallel Comput..

[17]  Michael Pidd,et al.  Hierarchical modular modelling in discrete simulation , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[18]  Pasquale Legato,et al.  Berth planning and resources optimisation at a container terminal via discrete event simulation , 2001, Eur. J. Oper. Res..

[19]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[20]  Pasquale Legato,et al.  The Quay Crane Deployment Problem At A Maritime Container Terminal , 2008 .

[21]  Gilbert Laporte,et al.  The service allocation problem at the Gioia Tauro Maritime Terminal , 2007, Eur. J. Oper. Res..

[22]  P. Legato,et al.  A Simulation Modelling Paradigm For The Optimal Management Of Logistics In Container Terminals , 2007 .

[23]  Kap Hwan Kim,et al.  A Quay Crane Scheduling Method Considering Interference of Yard Cranes in Container Terminals , 2006, MICAI.

[24]  Sigrún Andradóttir,et al.  Replicated batch means variance estimators in the presence of an initial transient , 2006, TOMC.

[25]  M. R. Irving,et al.  Observability Determination in Power System State Estimation Using a Network Flow Technique , 1986, IEEE Transactions on Power Systems.

[26]  N. Argon,et al.  Variance estimation using replicated batch means , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).