Container Terminal Throughput Research Articles

Container Terminal Digital Twin Yard System Construction

New requirements for terminal production and operation have emerged as a result of the increase in container terminal throughput. Traditional terminals’ manufacturing capabilities fall short of the expanding service needs. By constructing a digital twin yard for container terminals, the production capacity of terminals can be effectively improved, and the production operation process can be optimized. This paper firstly constructs a digital twin yard system for container terminals, proposing that it is mainly composed of physical space, virtual space, data, services, and intelligent agents. This paper elaborates on the core technologies of digital twin yards and finally takes the container delivery and loading process as an example to solve the production bottlenecks of the yard in the container delivery business by reorganizing the operation process and targeting it, which can improve the terminal production efficiency to a certain extent.

Research on the effect of number of yard trucks on container terminal throughput

Container terminal productivity is a benchmark for the level of effectiveness of a container port. One indicator of container terminal productivity level can be calculated through yard throughput. Several factors affect yard throughput, one of which is the number of truck terminals. This study aims to investigate the effect of the number of truck terminals on yard throughput at a container terminal. The investigation was conducted using a container terminal simulation which was based on the discrete event simulation method. As a case study, a simulation of a container terminal with a dock length of 300 m with several quay cranes was two units. The simulation was done by parameters adding the number of truck terminals and variations in the capacity of ships coming. The results of the simulation show that an increase in the number of truck terminals affects the number of outputs, depending on the size of the loading and unloading vessels. From the simulation results obtained, the largest yard throughput capacity of 809 containers with the number of truck terminals is 8 units on a shipload of 1,000 twenty-foot equivalent units. The results of this study provide an overview of the needs of the truck terminal in the container terminal. Highlights The yard throughput of the container terminal has been investigated by a simulation model built based on a discrete event simulation The discrete event simulation has obtained the effect of increasing the number of truck terminals on the yard throughputs with container capacity variations from 200 - 1,000 TEUs The increase in speed at the truck terminal is insignificant to increase yard throughput and necessary to do integrated scheduling between the truck terminals, quay cranes, and gantry cranes The increase in the quay crane operation in container terminals will decrease the dwell time of container throughput

Container port throughput analysis and active management using control theory

This paper deals with three-dimensional (3D) model of competitive Lotka-Volterra equation to investigate nonlinear dynamics and control strategy of container terminal throughput and capacity. Dynamical behaviors are intensely explored by using eigenvalue evaluation, bifurcation analysis, and time-series data. The dynamical analysis is to show the stability with bifurcation of the competition and collaboration of multiple container terminals in the maritime transportation. Based on the chaotic analysis, the sliding mode control theory has been utilized for optimization of port operations under disruptions. Extensive numerical simulations have been conducted to validate the efficacy and reliability of the presented control algorithms. Particularly, the closed-loop system has been assessed through chaotic suppression and synchronization strategies for port management. Finally, the presented fundamental techniques can be utilized to provide managerial insights and solutions on efficient seaport operations that allow more timely and cost-effective decision making for port authorities in such a highly competitive environment.

Stochastic modeling of parallel process flows in intra-logistics systems: Applications in container terminals and compact storage systems

Many intra-logistics systems, such as automated container terminals, distribution warehouses, and cross-docks, observe parallel process flows, which involve simultaneous (parallel) operations of independent resources while processing a job. When independent resources work simultaneously to process a common job, the effective service requirement of the job is difficult to estimate. For modeling simplicity, researchers tend to assume sequential operations of the resources. In this paper, we propose an efficient modeling approach for parallel process flows using two-phase servers. We develop a closed queuing network model to estimate system performance measures. Existing solution methods can evaluate the performance of closed queuing networks that consist of two-phase servers with exponential service times only. To solve closed queuing networks with general two-phase servers, we propose new solution methods: an approximate mean value analysis and a network aggregation dis-aggregation approach. We derive insights on the accuracy of the solution methods from numerical experiments. Although both solution methods are quite accurate in estimating performance measures, the network aggregation dis-aggregation approach consistently performs best. We illustrate the proposed modeling approach for two intra-logistic systems: a container terminal with automated guided vehicles and a shuttle-based compact storage system. Results show that approximating the simultaneous operations as sequential operations underestimates the container terminal throughput on average by 28% and at maximum up to 47%. Similarly, considering sequential operations of the resources in the compact storage system results in an underestimation of the throughput capacity up to 9%.

Stochastic Modelling of Parallel Process Flows in Intra-Logistics Systems: An Application in Automated Container Terminal

Many intra-logistics systems, such as automated container terminals, distribution warehouses, and crossdocks observe parallel process flows, which involve simultaneous (parallel) operations of multiple independent resources while processing a job. Due to the unknown service time of the job, stochastic modeling of the parallel process flows is quite complex. For modeling simplicity, researchers tend to assume sequential operations of the resources. This paper proposes a novel modeling approach using two-phase servers to model the simultaneous operations of resources. We develop a closed queuing network model to estimate the performance measures of a system which observes parallel process flows. To solve the resulting queuing network model, we construct two solution methods: an extended approximate mean value analysis and a network aggregation dis-aggregation approach. We derive insights on the accuracy of the solution methods using numerical experiments. Although both solution methods are quite accurate in estimating performance measures, the network aggregation dis-aggregation approach consistently performs best. We illustrate the proposed parallel modeling approach for two intra-logistic systems: an automated container terminal with automated guided vehicles and a robotic compact storage system. Results show that approximating the simultaneous operations as sequential operations underestimates the container terminal throughput on average by 28% and a maximum up to 47%. Similarly, considering sequential operations of the resources in the compact storage system results in underestimation of the throughput capacity up to 9%.

The quay crane scheduling problem with nonzero crane repositioning time and vessel stability constraints

The continuous growth in worldwide container terminals’ traffic resulted in an increasing interest for the Quay Crane Scheduling Problem (QCSP) in research and practice. This problem consists of scheduling the discharge and load operations of the containers of a vessel by a set of quay cranes; the objective is to minimize the completion time in an attempt to increase container terminal throughput. In the literature, most of the proposed studies focus on improving model formulation and solution methods with a trend, in most recent papers, to incorporate more realistic features of the problem. Despite the importance given by practitioners to vessel stability in scheduling discharge and load operations, there is little research that considers this constraint. This paper presents a novel MIP formulation of the QCSP that takes into account vessel stability constraints. Furthermore, the proposed model is very flexible in handling various settings of the QCSP, such as those related to crane traveling time, task preemption and unidirectional quay crane operating mode. In order to tackle problem complexity, a Genetic Algorithm (GA) is proposed. Computational results validate the MIP formulation on small-sized problems and highlight the performance of the proposed GA.

The Simulation and Study of the Operating System Efficiency Improvement of a Container Terminal

With the rapid expansion of Chinese container terminal throughput, each container terminal increasingly pays attention to the operational efficiency of storage yard. In this paper, aim at the problem of excessive containers relocation assignments to establish the slotting optimization allocation model, and verify the validity of the model by using Matlab; Using tandem queuing network modeling to build model of the container terminal operating system,and simulate the container terminal logistics system by Witness. It can provide a reference for efficiency improvement of the container terminal operating system.

A non-linear traffic flow-based queuing model to estimate container terminal throughput with AGVs

Efficient handling of containers at a terminal can reduce the overall vessel sojourn times and minimise operational costs. The internal transport of containers in these terminals is performed by vehicles that share a common guide path. The throughput capacity of a terminal may increase by increasing the number of vehicles; however, simultaneously congestion may reduce the effective vehicle speed. We model this situation accurately using a traffic flow-based closed queuing network model. The vehicle internal transport is modelled using a load-dependent server that captures the interaction between the number of vehicles in a transport segment and the effective vehicle speed. Using a non-linear traffic flow model, we show that the throughput reductions due to vehicle congestion can be as large as 85%. Hence, the effect of vehicle congestion during internal transport cannot be ignored. The model can also be used to determine the appropriate number of vehicles required to achieve the required terminal throughput by explicitly considering the effect of vehicle congestion.

World container port throughput follows lognormal distribution

We show in this paper that the throughput data for the top 300 container ports reported each year by the various authorities follows a simple truncated lognormal distribution. This surprising phenomenon repeats itself every year from 1982 to 2006, despite many tumultuous changes in the container shipping world. The empirical data suggests that Gibrat's Law of proportionate growth indeed holds for the world container throughput data. Unfortunately, the classical stochastic growth model and other variants often used to explain the origin of this law appears to be too simplistic for the container terminal industry. We use instead the perspective that the container terminal throughput data are essentially an aggregate measure of the number of visitations as each container circulates on the world shipping network, and use this to propose a Markov chain based container circulation model to explain the origin of this phenomenon. Simulation results show that our network-based model is able to replicate the behavior of the empirical data to a reasonable degree of accuracy, and does not contradict the law of proportionate growth. More importantly, this model is able to replicate the relationship between the degree of connectivity of a port (i.e. number of linkages with other ports) and its association with the container throughput data, an empirical regularity which could not be explained using classical approaches.

Discrete time model and algorithms for container yard crane scheduling

Container terminal (CT) operations are often bottlenecked by slow YC (yard crane) movements. PM (prime mover) queues in front of the YCs are common. Hence, efficient YC scheduling to reduce the PM waiting time is critical in increasing a CT's throughput. We develop an efficient model for YC scheduling by taking into account realistic operational constraints such as inter-crane interference, fixed YC separation distances and simultaneous container storage/retrievals. Among them, only inter-crane interference has ever been considered in the literature. The model requires far fewer integer variables than the literature by using bi-index decision variables. We show how the model can be solved quickly using heuristics and rolling-horizon algorithm, yielding close to optimal solutions in seconds. The solution quality and solution time are both better than the literature even with additional constraints considered. The proposed formulations and algorithms can be extended to other problems with time windows and space constraints.