Stochastic Modeling of Unloading and Loading Operations at a Container Terminal using Automated Lifting Vehicles

With growing worldwide trade, container terminals have grown in number and size. Many new terminals are now automated to increase operational efficiency. The key focus is on improving seaside processes, where a distinction can be made between single quay crane operations (all quay cranes are either loading or unloading containers) and overlapping quay crane operations (some quay cranes are loading while others are unloading containers). From existing studies, it is not clear if the design insights obtained from analyzing single operations, such as optimal stack layout, are consistent with the insights obtained from analyzing overlapping operations. In this paper, we develop new integrated stochastic models for analyzing the performance of overlapping loading and unloading operations that capture the complex stochastic interactions among quayside, vehicle, and stackside processes. Using these integrated models, we are able to show that that there are stack layout configurations that are robust for both single (either loading or unloading) and for overlapping (both loading and unloading) operations

Modeling and Design of Container Terminal Operations

Design of container terminal operations is complex because multiple factors affect the operational perfor- mance. These factors include: topological constraints, a large number of design parameters and settings, and stochastic interactions that interplay among the quayside, vehicle transport, and stackside processes. In this research, we propose new integrated queuing network models for rapid design evaluation of container terminals with Automated Lift Vehicles (ALVs) and Automated Guided Vehicles (AGVs). These models offer the flexibility to analyze alternate design variations and develop insights. For instance, the effect of alternate vehicle dwell point policy is analyzed using state-dependent queues, whereas the efficient terminal layout is determined using variation in the service time expressions at the stations. Further, using embedded Markov chain analysis, we develop an approximate procedure for analyzing bulk container arrivals. These models form the building block for design and analysis of large-scale terminal operations. We test the model efficacy using detailed in-house simulation experiments and real-terminal validation by partnering with an external party

Sea Container Terminals

Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations

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

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 resou

A Fluid Flow Queuing Network Model for Performance Analysis of Bulk Liquid Terminals

Bulk liquid terminals play a crucial role in enabling the timely discharge and loading of liquid from the tankers and also facilitating oil transport to the hinterland via pipelines and external trucks. However, the speed of operations (and demurrage costs in the likely event of vessel handling delays) depends on the capacities of all terminal resources including berth, loading arms, and storage tank farms. Today, there is a limited understanding of how the interactions among the resources affect the overall vessel sojourn time performance. Using an integrated fluid flow simulation model of a bulk liquid terminal, we are able to gain much insight into the discharge operations of a tanker, in particular, implications of storage tank capacity feedback on the loading arm utilization and vessel sojourn times

The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research