Abstract

Increasing international maritime transport drives the need for efficient container terminals. The speed at which containers can be processed through a terminal is an important performance indicator. In particular, the productivity of the quay cranes (QCs) determines the performance of a container terminal; hence QC scheduling has received considerable attention. This article develops a comprehensive model to represent the waterside operations of a container terminal. Waterside operations comprise single and twinlift handling of containers by QCs, automated guided vehicles and yard cranes. In common practice, an uncoordinated scheduling heuristic is used to dispatch the equipment operating on a terminal. Here, uncoordinated means that the different machines that operate in the container terminal seek optimal productivity solely considering their own respective stage. By contrast, our model provides a coordinated schedule in which operations of all terminal equipment can be considered at once to achieve productivity closer to the QC optimal. The model takes the form of a hybrid flow shop (HFS) with novel features for bi-directional flows and job pairing. The former enables jobs to move freely through the HFS in both directions; the latter constrains certain jobs to be performed simultaneously by a single machine. We solve the coordinated model by means of a tailored simulated annealing (SA) algorithm that balances solution quality and computational time. We empirically study time-bounded variants of SA and compare them with a branch-and-bound algorithm. We show that our approach can produce coordinated schedules for a terminal with up to eight QCs in near real time.

Highlights

  • Worldwide today, 60% of the total cargo volume is transported in standardized boxes called twenty-feet equivalent units (TEU) containers (Stahlbock and Voß 2008)

  • The first term derives from the starting time variables of the various equipment items; the second term derives from the sequence variables required for the first container at the automated guided vehicles (AGVs) and rack stage; the third term derives from the sequence variables at the yard cranes (YCs) stage; the final term derives from the remaining sequence variables at the AGV and rack stage

  • We study the performance versus the theoretical optimum, obtained with the exact solution technique Branch and Bound (B&B)

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Summary

Introduction

60% of the total cargo volume is transported in standardized boxes called twenty-feet equivalent units (TEU) containers (Stahlbock and Voß 2008). The function of a port container terminal is to trans-ship such TEU containers: deep-sea vessels, barges, trains and trucks arrive to the terminal to deliver or collect containers. The turnaround time of deep-sea vessels is used to determine the performance of a terminal (Bish 2003; De Koster et al 2009). The optimization-based, non-heuristic scheduling models currently available do not match with the operations on a container terminal, because twinlift operations are ignored and simultaneous loading and unloading of multiple ships is not considered. The available optimization models cannot be used in near real-time—whereas computational times should remain low since re-planning is often required at a container terminal, since the current situation can change rapidly.

Background and problem formulation
Related work
Model formulation
Model description
Objective
Decision variables
Constraints and objective
Solution technique
Initial solution
Selection
Neighbourhood generation
Repair
Solution evaluation
Acceptance criterion
Stopping criterion
Experimental results
Conclusion and outlook
Full Text
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