Hybrid model predictive control for equipment in an automated container terminal

Over the last decades, there has been a significant growth of global freight transport due to the enormous commercial trade. Over 60% of worldwide deep-sea cargo is transported by containers. The increased amount of containers that arrive and depart with container ships provides much pressure for terminal operators. The throughput, i.e., the number of containers handled per hour, should be increased. A container terminal is characterized by a large number of pieces of equipment that operate in a dynamically changing environment. The transport of a container depends on the actions of multiple pieces of equipment that are physically spread all over the container terminal. We are investigating how to effectively manage the volume growth by considering a more integrated way of looking at transport of freight. In particular in this paper, we propose to use the hybrid automaton modeling framework for modeling the handling of containers. Model predictive control is proposed for achieving the desired performance.

[1]  André Langevin,et al.  Multiple yard cranes scheduling for loading operations in a container terminal , 2011 .

[2]  Youfang Huang,et al.  A quay crane dynamic scheduling problem by hybrid evolutionary algorithm for berth allocation planning , 2009, Comput. Ind. Eng..

[3]  Der-Horng Lee,et al.  Synchronization of yard truck scheduling and storage allocation in container terminals , 2009 .

[4]  Jaap A. Ottjes,et al.  USING CONTAINER CALL TIME INFORMATION FOR RESTACKING REDUCTION , 2008 .

[5]  Teodor Gabriel Crainic,et al.  Chapter 8 Intermodal Transportation , 2007, Transportation.

[6]  Jaap A. Ottjes,et al.  A SIMULATION MODEL FOR AUTOMATED CONTAINER TERMINALS , 2000 .

[7]  Alberto Bemporad,et al.  Control of systems integrating logic, dynamics, and constraints , 1999, Autom..

[8]  Stefan Voß,et al.  Operations research at container terminals: a literature update , 2007, OR Spectr..

[9]  Kap Hwan Kim,et al.  A scheduling method for Berth and Quay cranes , 2003 .

[10]  Li Pheng Khoo,et al.  A distributed agent system for port planning and scheduling , 2011, Adv. Eng. Informatics.

[11]  Michael G.H. Bell,et al.  An uncertainty-aware AGV assignment algorithm for automated container terminals , 2010 .

[12]  Thomas A. Henzinger,et al.  The theory of hybrid automata , 1996, Proceedings 11th Annual IEEE Symposium on Logic in Computer Science.

[13]  Maurizio Bielli,et al.  Object oriented model for container terminal distributed simulation , 2006, Eur. J. Oper. Res..

[14]  Matthew E. H. Petering Decision support for yard capacity, fleet composition, truck substitutability, and scalability issues at seaport container terminals , 2011 .

[15]  Francesca Contu,et al.  A model for performance evaluation and sensitivity analysis of seaport container terminals , 2011 .

[16]  Martin W. P. Savelsbergh,et al.  Minimum Vehicle Fleet Size Under Time-Window Constraints at a Container Terminal , 2005, Transp. Sci..

[17]  Alberto Bemporad,et al.  HYSDEL-a tool for generating computational hybrid models for analysis and synthesis problems , 2004, IEEE Transactions on Control Systems Technology.

[18]  Cristiano Cervellera,et al.  Modeling and Feedback Control for Resource Allocation and Performance Analysis in Container Terminals , 2008, IEEE Transactions on Intelligent Transportation Systems.

[19]  Paul Davidsson,et al.  Agent Based Simulation Architecture for Evaluating Operational Policies in Transshipping Containers , 2006, MATES.