Deadlock prevention for automated guided vehicles in automated container terminals

With the continuous increase in labour costs and the demands of the supply chain, improving the efficiency of automated container terminals has been a key factor in the development of ports. Automated guided vehicles (AGVs) are the main means of horizontal transport in such terminals, and problems in relation to their use such as vehicle conflict, congestion and waiting times have become very serious, greatly reducing the operating efficiency of the terminals. In this article, we model the minimum driving distance of AGVs that transport containers between quay cranes (QCs) and yard cranes (YCs). AGVs are able to choose the optimal path from pre-planned paths by testing the overlap rate and the conflict time. To achieve conflict-free AGV path planning, a priority-based speed control strategy is used in conjunction with the Dijkstra depth-first search algorithm to solve the model. The simulation experiments show that this model can effectively reduce the probability of AGVs coming into conflict, reduce the time QCs and YCs have to wait for their next task and improve the operational efficiency of AGV horizontal transportation in automated container terminals.

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Research on Cooperative Scheduling of Automated Quayside Cranes and Automatic Guided Vehicles in Automated Container Terminal

Mathematical Problems in Engineering ◽

10.1155/2019/6574582 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15

Author(s):

Qianru Zhao ◽

Shouwen Ji ◽

Dong Guo ◽

Xuemin Du ◽

Hongxuan Wang

Keyword(s):

Search Algorithm ◽

Container Terminal ◽

Container Terminals ◽

Automated Guided Vehicles ◽

Total Energy Consumption ◽

Two Stage ◽

Taboo Search ◽

Automatic Guided Vehicles ◽

Collaborative Scheduling ◽

Taboo Search Algorithm

According to previous research studies, automated quayside cranes (AQCs) and automated guided vehicles (AGVs) in automated container terminals have a high potential synergy. In this paper, a collaborative scheduling model for AQCs and AGVs is established and the capacity limitation of the transfer platform on AQCs is considered in the model. The minimum total energy consumption of automated quayside cranes (AQCs) and Automatic Guided Vehicles (AGVs) is taken as the objective function. A two-stage taboo search algorithm is adopted to solve the problem of collaborative scheduling optimization. This algorithm integrates AQC scheduling and AGV scheduling. The optimal solution to the model is obtained by feedback from the two-stage taboo search process. Finally, the Qingdao Port is taken as an example of a data experiment. Ten small size test cases are solved to evaluate the performance of the proposed optimization methods. The results show the applicability of the two-stage taboo search algorithm since it can find near-optimal solutions, precisely and accurately.

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Performance Analysis on Transfer Platforms in Frame Bridge Based Automated Container Terminals

Mathematical Problems in Engineering ◽

10.1155/2013/593847 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Hongtao Hu ◽

Byung Kwon Lee ◽

Youfang Huang ◽

Loo Hay Lee ◽

Ek Peng Chew

Keyword(s):

Markov Chain ◽

Performance Analysis ◽

Markov Chain Model ◽

Container Terminal ◽

Container Terminals ◽

Automated Guided Vehicles ◽

Chain Model ◽

Quay Cranes ◽

Automated Container Terminal ◽

Multistory Frame

This paper studies a new automated container terminal (ACT) system which utilizes multistory frame bridges and rail-mounted trolleys to transport containers between the quay and the yard. Beside typical ACT systems use trucks or automated guided vehicles for transporting containers between quay cranes and yard cranes, the new design uses three types of handling machines, namely, ground trolleys (GTs), transfer platforms (TPs), and frame trolleys (FTs). These three types of handling machines collaborate with one another to transport containers. This study decomposes the system into several subsystems. Each subsystem has one TP and several FTs and GTs dedicated to this TP. Then, a Markov chain model is developed to analyze the throughput of TPs. At last, the performance of the new ACT system is estimated. Sensitivity analyzes the numbers, and the processing rates of trolleys are conducted through the numeric experiments.

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Anisotropic Q-learning and waiting estimation based real-time routing for automated guided vehicles at container terminals

Journal of Heuristics ◽

10.1007/s10732-020-09463-9 ◽

2021 ◽

Author(s):

Pengfei Zhou ◽

Li Lin ◽

Kap Hwan Kim

Keyword(s):

Real Time ◽

Container Terminals ◽

Automated Guided Vehicles ◽

Q Learning

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Modeling and Design of Container Terminal Operations

Operations Research ◽

10.1287/opre.2019.1920 ◽

2020 ◽

Vol 68 (3) ◽

pp. 686-715 ◽

Cited By ~ 1

Author(s):

Debjit Roy ◽

René De Koster ◽

René Bekker

Keyword(s):

Large Scale ◽

Container Terminal ◽

Heavy Traffic ◽

Building Blocks ◽

Transport Processes ◽

Container Terminals ◽

Automated Guided Vehicles ◽

Design Parameters ◽

Container Terminal Operations ◽

Terminal Operations

The design of container terminal operations is complex because multiple factors affect operational performance. These factors include numerous choices for handling technology, terminal topology, and design parameters and stochastic interactions between the quayside, stackside, and vehicle transport processes. In this research, we propose new integrated queuing network models for rapid design evaluation of container terminals with automated lift vehicles and automated guided vehicles. These models offer the flexibility to analyze alternate design variations and develop insights. For instance, the effect of different vehicle dwell point policies and efficient terminal layouts are analyzed. We show the relation among the dwell point–dependent waiting times and also show their asymptotic equivalence at heavy traffic conditions. These models form the building blocks for design and analysis of large-scale terminal operations. We test the model efficacy using detailed simulation experiments and real-terminal validation.

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