Optimal Stack Layout in a Sea Container Terminal with Automated Lifting Vehicles

Debjit Roy, Akash Gupta, Sampanna Parhi and René de Koster

ERIM Report Series Research in Management from Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam

Abstract: Container terminal performance is largely determined by its design decisions, which include the number and type of quay cranes (QCs), stack cranes (SCs), transport vehicles, vehicle travel path, and stack layout. The terminal design process is complex because it is affected by factors such as topological constraints, stochastic interactions among the quayside, vehicle transport and stackside operations. Further, the orientation of the stack layout (parallel or perpendicular to the quayside) plays an important role in the throughput time performance of the terminals. Previous studies in this area typically use deterministic optimization or probabilistic travel time models to analyze the effect of stack layout on terminal throughput times, and ignore the stochastic interactions among the resources. It is unclear if stochastic interactions have an impact on the optimal stack layout. In this research, we capture the stochasticity with an integrated queuing network modeling approach to analyze the performance of container terminals with parallel stack layout using automated lifting vehicles (ALVs). Using this model, we investigate 1008 parallel stack layout configurations in terms of throughput times and determine the optimal stack layout configuration. We also find that, assuming an identical width of the internal transport area, container terminals with parallel stack layout perform better (from 4% - 12% in terms of container throughput times) than terminals with a perpendicular stack layout.

Keywords: container terminals; optimal stack layout; parallel versus perpendicular stack orientation; seaside operations; queuing model (search for similar items in EconPapers)
Pages: 54
Date: 2014-09-03
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Citations: View citations in EconPapers (2)

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