Using simulation to optimize transhipment systems: Applications in field
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Using simulation to optimize transhipment systems: Applications in field R o b e r t o C i g o l i n i a, M a r g h e r i t a P e r o a, T o m m a s o R o s s i b a n d Andrea Sianesia a
School of Management, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133, Milan, Italy. E-mails: [email protected]; [email protected]; [email protected] b Institute of Technology, Università Carlo Cattaneo – LIUC, Castellanza (VA), Italy. E-mail: [email protected]
Abstract
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.
Maritime Economics & Logistics (2013) 15, 332–348. doi:10.1057/mel.2013.8
Keywords: simulation; fleet management; performance measurement; model building automation; maritime transportation; transhipment
Introduction Transhipment, in the context of this article, means a way to load (or unload) an Ocean Going Vessel (OGV) by means of a Floating Transfer Station (FTS), that is, a ship provided with storage space, cranes and conveyors that is fed by (or feeds) © 2013 Macmillan Publishers Ltd. 1479-2931 Maritime Economics & Logistics www.palgrave-journals.com/mel/
Vol. 15, 3, 332–348
Using simulation to optimize transhipment systems
the barges, which perform a there-and-back service between the port and the FTS. Sizing a transhipment system is one of the main challenges logistics service providers face in their day-by-day activity, and it is definitely a unique task for several reasons. First of all, it involves a trade-off between conflicting goals: basically, satisfying customer requirements with the cheapest (or even affordable) transhipment system. Then, a number of decision variables and constraints need to be considered. Decision variables refer to: FTS capacity, number of cranes and their loading (and discharging) rate, number of barges, their capacity and speed, and – if barges are not self-propelled – number of tugs (with their speed) and, finally, the number of port cranes that can be potentially used, with their loading and discharging rate. Constraints are both static and dynamic. Static constraints are given by the number of piers, by the times the barges spend in hauling at piers and at the FTS a
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