Delgado-Ortegon, Alberto

Guilbert, Nicolas

Jensen, Rune Møller

Nowadays a considerable part of trade goods are seaborne, with around 90% of all non-bulk cargo carried by container vessels. These vessels follow specific routes loading and unloading containers at different ports. A Stowage Plan (SP) assigns containers to load in a port to vessel slots. Good SPs save port fees, optimize use of vessel capacity, and reduce bunker consumption. Stowage Coordinators (SCs) produce these plans manually with the help of graphical tools, but high-quality SPs are hard to generate with the limited support they provide. In this abstract, we introduce AngelStow which is a commercial optimization-based decision support tool for stowing container vessels developed in collaboration between Ange Optimization and The IT University of Copenhagen. The tool assists SCs in the process of generating SPs interactively, focusing on satisfying and optimizing constraints and objectives that are tedious to deal with for humans, while letting the SCs use their expertise to deal with hard combinatorial objectives and corner cases

The IT University of Copenhagen's Repository

AngelStow: A Commercial Optimization-BasedDecision Support Tool for Stowage PlanningAlberto Delgado1, Rune Møller Jensen1, and Nicolas Guilbert21 IT University of Copenhagen, Denmark, {alde,rmj}@itu.dk2 Ange Optimization, Denmark, nicolas@ange.dkNowadays a considerable part of trade goods are seaborne, with around 90%of all non-bulk cargo carried by container vessels. These vessels follow specificroutes loading and unloading containers at different ports. A Stowage Plan (SP)assigns containers to load in a port to vessel slots. Good SPs save port fees,optimize use of vessel capacity, and reduce bunker consumption. Stowage Co-ordinators (SCs) produce these plans manually with the help of graphical tools(e.g., [4,3]), but high-quality SPs are hard to generate with the limited sup-port they provide. Plans for deep-sea vessels with capacities up to 15.000 TEUs(Twenty-foot Equivalent Units) are made just hours before the vessel calls theport, and it is difficult to satisfy stacking rules, stress limits, and stability re-quirements, while optimizing the use of resources e.g., port cranes and vesselcapacity. Moreover, the SP for the current port affects SPs in downstream ports.Liner shipping companies are in their infancy on using optimization-baseddecision support tools for this task. Moreover, most of the research on stowageplanning during the past twenty years has focused on algorithms to automati-cally generate stowage plans (e.g., [2,1,6,5]), limiting the role of expert users toresult validation. Though some of these approaches generate good quality SPs,difficulties arise using them in practice. Stowage planning is full of corner casesdisregarded in the models solved by automatic approaches. SCs need to be ableto adapt automatic generated plans to specific situations, or be directly involvedin the generation of the plans, such that SPs can be successfully used in practice.In this abstract, we introduce AngelStow which is a commercial optimization-based decision support tool for stowing container vessels developed in a collabo-ration between Ange Optimization and The IT University of Copenhagen. Thetool assists SCs in the process of generating SPs interactively, focusing on satis-fying and optimizing constraints and objectives that are tedious to deal with forhumans, while letting the SCs use their expertise to deal with hard combinato-rial objectives and corner cases. Our approach divides the process of generatingSPs into two major phases as depicted in left figure of Figure 1. In the firstphase, the SC selects the discharge port of containers to be stowed and a weightcapacity for each longitudinal section of the vessel (Bays). Containers can alsobe manually placed in specific slots at will by the SCs in order to address cornercases. In the second phase, following the layout proposed by the SC, containersfrom the load list are stowed automatically in slots by an approach combining anLP model and a placement heuristic, fulfilling low level stacking constraints andminimizing the capacity wasted. The algorithm stows each port on the vessel’sroute independently, and the results are propagated to downstream ports.AutomaticStowage    LayoutCoordinatorAdjustmentManualyesAdjustmentBallastnoyes  GoodStowage?Stable?noFig. 1. Left: flow diagram for generating SPs with AngelStow. Right: screen shot ofAngelStow stowing a vessel calling 5 ports. Labels indicate load list of containers andstatus of the ballast tanks for the current call, and multi-port vessel view of 5 differentcalls (current call highlighted) with their corresponding stresses graphs.Plans produced in the second phase are easily modified by SCs to cover cornercases. Once a satisfactory SP is generated, a decision support tool for adjustingballast water can be run to stabilize the vessel. This tool solves an LP modelminimizing the amount of water filled into the tanks such that all main stressesare within limits (torsion, bending, shear force), a desired trim is achieved, andthe vessel is not tilted. The layout defined by the SC can be modified at any time,and the generation process starts over. For a good level of interaction with SCs,execution time for the decision support tools is kept sub-second. Right figure ofFigure 1 depicts AngelStow being used for stowing a vessel.References1. Daniela Ambrosino, Davide Anghinolfi, Massimo Paolucci, and Anna Sciomachen.An experimental comparison of different heuristics for the master bay plan problem.In Proceedings of the 9th Int. Symposium on Experimental Algorithms, pages 314–325, 2010.2. Mehmet Gumus, Philip Kaminsky, Erik Tiemroth, and Mehmet Ayik. A multi-stagedecomposition heuristic for the container stowage problem. In Proceedings of the2008 MSOM Conference, 2008.3. Interschalt. Seacos. www.interschalt.de, 2011.4. Navis. PowerStow. www.navis.com, 2011.5. Dario Pacino, Alberto Delgado, Rune M. Jensen, and Tom Bebbington. Fast gen-eration of near-optimal plans for eco-efficient stowage of large container vessels. InTo appear in Proceedings of the Second International Conference on ComputationalLogistics. Springer, 2011.6. Malcolm Yoke, Hean Low, Xiantao Xiao, Fan Liu, Shell Ying Huang, Wen Jing Hsu,and Zhengping Li. An automated stowage planning system for large containerships.In In Proceedings of the 4th Virtual Int. Conference on Intelligent Production Ma-chines and Systems, 2009.

AngelStow: A Commercial Optimization-Based Decision Support Tool for Stowage Planning

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