Container Terminal Logistics Research Articles

Application Research on Real-Time Interactive Simulation System of Container Yard Based on Unity3D

In view of the limitations of the mathematical method used in the container terminal logistics system, this paper uses Unity3D to establish a computer simulation model for the container automated yard, which dynamically displays the operation process of the container automated yard logistics system in real time. Through the plane four-parameter coordinate conversion method and by taking the Shanghai urban construction coordinate system as the medium, it completes the conversion from the satellite positioning reference ellipsoid coordinates to the three-dimensional virtual scene coordinates. The example results show that the method is reliable and practical, improves the accuracy and efficiency of positioning, and provides a reliable reference basis for the container terminal logistics system.

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

The synergy of computational logistics and deep learning provides a new methodology and solution to the operational decisions of container terminal handling systems (CTHS) at the strategic, tactical, and executive levels. Above all, the container terminal logistics tactical operational complexity is discussed by computational logistics, and the liner handling volume (LHV) has important influences on a series of terminal scheduling decision problems. Subsequently, a feature-extraction-based lightweight convolutional and recurrent neural network adaptive computing model (FEB-LCR-ACM) is presented initially to predict the LHV by the fusion of multiple deep learning algorithms and mechanisms, especially for the specific feature extraction package of tsfresh. Consequently, the container-terminal-oriented logistics service scheduling decision support design paradigm is put forward tentatively by FEB-LCR-ACM. Finally, a typical large-scale container terminal of China is chosen to implement, execute, and evaluate the FEB-LCR-ACM based on the terminal running log around the indicator of LHV. In the case of severe vibration of LHV between 2 twenty-foot equivalent units (TEUs) and 4215 TEUs, while forecasting the LHV of 300 liners by the log of five years, the forecasting error within 100 TEUs almost accounts for 80%. When predicting the operation of 350 ships by the log of six years, the forecasting deviation within 100 TEUs reaches up to nearly 90%. The abovementioned two deep learning experimental performances with FEB-LCR-ACM are so far ahead of the forecasting results by the classical machine learning algorithm that is similar to Gaussian support vector machine. Consequently, the FEB-LCR-ACM achieves sufficiently good performance for the LHV prediction with a lightweight deep learning architecture based on the typical small datasets, and then it is supposed to overcome the operational nonlinearity, dynamics, coupling, and complexity of CTHS partially.

Simulation Model to Determine Ratios between Quay, Yard and Intra-Terminal Transfer Equipment in an Integrated Container Handling System

This paper presents a generic simulation model to determine the equipment mix (quay, yard and intra-terminal transfer) for a Container Terminal Logistics Operations System (CTLOS). The simulation model for the CTLOS, a typical type of discrete event dynamic system (DEDS), consists of three sub-models: ship queue, loading-unloading operations and yard-gate operations. The simulation model is empirically applied to phase 1 of the Yangshan Deep Water Port in Shanghai. This study considers different scenarios in terms of container throughput levels, equipment utilization rates, and operational bottlenecks, and presents a sensitivity analysis to evaluate and choose reasonable equipment ratio ranges under different operational conditions.

Computational Logistics for Container Terminal Logistics Hubs Based on Computational Lens and Computing Principles

The volatile global shipping market puts forward the higher requirements to the container terminal logistics systems (CTLS) than ever, especially in terms of programming, planning, scheduling and decision. The computational logistics provide a systematic methodology to overcome the issues that differs from traditional approaches distinctly. This paper discusses the theoretical framework, important components and core concept of computational logistics, and then presents the container terminal logistics generalized computation design, implement, execution, analysis and evaluation hierarchy (LGC-DIE-AEH) by the integration of the computational lens and computing principles to explore the computational logistics in the field of CTLS. Subsequently, the execution performance of a quay crane farm at a regional container hub terminal in China is analyzed and evaluated by LGC-DIE-AEH. The crane performance evaluation core indicator framework (CPE-CIF) is proposed by the fusion of the design philosophy and underlying principles of computing architecture, operating system, and virtual machine by computational lens. The CPE-CIF help us to find out the advantages, disadvantages and improvement directions of quay crane farm operation. That illustrates and verifies the feasibility and credibility of LGC-DIE-AEH from the perspective of the practice of container terminal decision-making support at the tactical level.

Container Terminal Logistics Generalized Computing Architecture and Green Initiative Computational Pattern Performance Evaluation

Container terminals are the typical representatives of complex supply chain logistics hubs with multiple compound attributes and multiple coupling constraints, and their operations are provided with the strong characteristics of dynamicity, nonlinearity, coupling, and complexity (DNCC). From the perspective of computational logistics, we propose the container terminal logistics generalized computing architecture (CTL-GCA) by the migration, integration, and fusion of the abstract hierarchy, design philosophy, execution mechanism, and automatic principles of computer organization, computing architecture, and operating system. The CTL-GCA is supposed to provide the problem-oriented exploration and exploitation elementary frameworks for the abstraction, automation, and analysis of green production at container terminals. The CTL-GCA is intended to construct, evaluate, and improve the solution to planning, scheduling, and decision at container terminals, which all are nondeterministic polynomial hard problems. Subsequently, the logistics generalized computational pattern recognition and performance evaluation of a practical container terminal service case study is launched by the qualitative and quantitative approach from the sustainable perspective of green production. The case study demonstrates the application, utilization, exploitation, and exploration of CTL-GCA preliminarily, and finds the unsustainable patterns of production at the container terminal. From the above, we can draw the following conclusions. For one thing, the CTL-GCA makes a definition of the abstract and automatic running architecture of logistics generalized computation for container terminals (LGC-CT), which provides an original framework for the design and implementation of control and decision mechanism and algorithm. For another, the CTL-GCA can help us to investigate the roots of DNCC thoroughly, and then the CTL-GCA makes for conducting the efficient and sustainable running pattern recognition of LGC-CT. It is supposed to provide a favorable guidance and supplement to define, design, and implement the agile, efficient, sustainable, and robust task scheduling and resource allocation for container terminals by computational logistics whether in the strategy level or the tactical one.

Container Terminal Oriented Logistics Generalized Computational Complexity

The planning and scheduling of container terminal logistics systems (CTLS) are the multi-objective and multiple strong constraints combinatorial optimization challenges under the uncertain environments, and those are provided with high goal orientation, dynamics, context-sensitivity, coupling, timeliness, and complexity. The increasingly sophisticated decision-making for CTLS is one of the most pressing problems for the programming and optimization method available. This paper discusses CTLS in terms of logistics generalized computation complexity based on computational thinking, great principles of computing, and computational lens, which three are abbreviated with 3CTGPL, and makes a definition of container terminal oriented logistics generalized computational complexity (CTO-LGCC) and container terminal logistics generalized computation comprehensive performance perspective (CTL-GCCPP) from the dimensions of time, space, communication, processor, and memory access. Both can analyze, generalize, migrate, translate, localize, modificate, and evaluate the above-complicated problems and lay solid foundations and establish a feedback improvement framework for the computational model and scheduling algorithms of the CTLS, which is an essential complement to the modeling and optimization methodology and solutions to CTLS with computational logistics. Finally, aimed at the logistics service cases for a large-scale container terminal, the simulation is designed and implemented for different scheduling algorithms, and the qualitative and quantitative comprehensive analysis is executed for the concomitant CTO-LGCC that demonstrates and verifies the feasibility and credibility of the CTO-LGCC and CTL-GCCPP from the viewpoint of the practice of container terminal decision-making support on the tactical level.

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.

Queuing Model of Container Terminal Logistics System in Event Scheduling

event scheduling is mainly adopted for the queueing model of logistics system. Event scheduling records the selected events according to the earliest occurring time of the next event by timing module and converts to subprogram treated by the event. Keywords:event scheduling,queueing model

Simulation and Visual Operation: IT Application on Container Terminal Production Logistics System

This paper introduces a solution to process control and visualized management system of container terminal. Some advanced technologies, such as GIS, GPS, WLAN, DLP screen etc, are integrated into the Container Terminal Production Logistics System in the solution. Another innovation is the auto-lock equipment fixed on RTG. IT application on container terminal, which includes accurate positioning, dynamic tracking, process controlling, distance operating, and this solution is able to effectively avoid the drivers operational misplay.

선박 물류 프로세스의 실시간 서비스 완료시간 예측에 대한 연구

물류 시스템은 다양한 프로세스에 걸친 한정된 자원들의 작업을 통해 고객에게 서비스를 제공한다. 이와 같은 복잡한 환경에서 높은 서비스 수준을 유지하기 위해서는 프로세스 진행 상황에 대한 실시간 모니터링과 그를 통한 성과 달성도의 지속적인 관리가 필수적으로 요구된다. 본 연구에서는 컨테이너 터미널을 대상으로 선박 물류 프로세스의 서비스 완료시간을 예측하는 방법론을 제시한다. 이를 위해 먼저 선박에서 컨테이너를 싣고 내리는 프로세스에 영향을 미치는 요인을 파악한다. 그리고, 확인된 요인들 중 영향 정도를 직접적으로 측정하기 어려운 것에 대해 정량적인 값을 추정하는 방안을 개발한다. 서비스 완료 시간에 대한 실시간 예측은 의사결정 나무를 사용한다. 과거 프로세스 진행 이력을 바탕으로 의사결정 나무를 학습시킨 후, 특정 시점에서 실시간 요인의 상태를 이용하여 서비스 완료시간을 예측한다. 컨테이너 터미널 시뮬레이션 모형을 활용하여 본 연구에서 제시하는 방법론을 검증하였다. Logistics systems provide their service to customers by coordinating the resources with limited capacity throughout the underlying processes involved to each other. To maintain the high level of service under such complicated condition, it is essential to carry out the real-time monitoring and continuous management of logistics processes. In this study, we propose a method of estimating the service completion time of key processes based on process-state information collected in real time. We first identify the factors that influence the process completion time by modeling and analyzing an influence diagram, and then suggest algorithms for quantifying the factors. We suppose the container terminal logistics and the process of discharging and loading containers to a vessel. The remaining service time of a vessel is estimated using a decision tree which is the result of machine-learning using historical data. We validated the estimation model using container terminal simulation. The proposed model is expected to improve competitiveness of logistics systems by forecasting service completion in real time, as well as to prevent the waste of resources.

Research on the Energy Consumption Evaluation Model of Container Terminal Logistics System

In this paper, energy consumption evaluation index system of the container terminal logistics system is briefly studied. Energy consumption is divided into two parts as dynamic and static energy consumption. Then an effective energy consumption evaluation model of container terminal logistics system is established.

Simulation-based optimization in port logistics

This is the summary of the author’s PhD thesis supervised by Pasquale Legato and defended on March 4, 2009 at the Universita della Calabria in Rende, Italy. The thesis is written in English and is available from rmazza@deis.unical.it upon request. Container terminal logistics have received great interest in the scientific literature from both the theoretical and practical standpoint. The reason for such concern is straightforward if one considers the number and randomnature of the operational activities carried-out in these facilities: vessel arrival and berthing, resource assignment and scheduling, container transfer and handling, emergency management (e.g. equipment failure, congestion phenomena, weather conditions) and so on. The common approach to problem design and solution in these logistic platforms is based on decomposing the original problem into several smaller models. However, to deliver overall system performance measures, a satisfactory contribution can spring from the use of an integrated methodology which can significantly aid decision-making under uncertainty: simulation-based optimization (SO). The SO methodological contribution proposed herein arises from considering the integration of stand-alone optimization techniques in discrete-event simulation, in order to first generate and then evaluate alternative solutions for managing dynamic logistic processes, under realistic conditions of uncertainty. As for solution generation, a metaheuristic approach based on a variant of the wellknown Simulated Annealing (SA) algorithm (see M.H. Alrefaei and S. Andradttir, “A simulated annealing algorithm with constant temperature for discrete stochastic optimization”, Management Science 45(5), 748–764, 1999) has been adopted. Besides discarding the basic assumption according to which the temperature Tk → 0 as k → 0 by assuming Tk = T ∀k, this approach bears two possible ways of estimating the optimum solution. It either uses the most visited solution or selects the solution with the best average estimated value of the objective function. The thesis first discusses the effectiveness of this constant temperature approachwhich

Study on Modeling of Container Terminal Logistics System Using Agent-Based Computing and Knowledge Discovery

Container terminal is the transfer hinge of land-sea intermodal transportation. Container terminal logistics system (CTLS) is the central handling platform of container logistics in logistics network as the computer system is the core processing node to information flow in internet. Quay side is its “central processing unit (CPU)” of handling platform and storage yard is its “main memory.” CTLS is a highly complex system which includes parallel, negotiation, and competition relations etc. in operation so that this paper introduces agent based computing (ABC) and knowledge discovery in database (KDD) to model CTLS. Logistics, information flow, and decision flow are the kernel of transaction and operation of container terminals which are developing into professional third party logistics (TPL) and seized of enormous business data which are accumulated by the inner information infrastructure and the public electronic commerce (EC) platform. Through the analysis to the handling operation and container logistics of CTLS, we refer to the design thinking of computer organization and architecture and introduce the resource scheduling thinking of the operation system, and draw an analogy between CTLS and parallel computer architecture based on Harvard architecture, map and carve up the production, and scheduling system of CTLS into fourteen kinds of agents: berth allocation agent, quay crane allocation agent, storage yard management agent, yard crane allocation agent, container truck dispatch agent, tugboat dispatch agent, gate house management agent, berth agent, quay crane agent, yard agent, yard crane agent, container truck agent, tugboat agent, and gate house agent according to the entities and their mutual relations. We bring forward the modeling framework of CTLS based on multi-agent and KDD which provides the engine to drive the model accordingly. This modeling thinking and framework, which serves the turn to the queuing theory inherently and possesses the top-ranking flexibility and robustness to the computational intelligence (CI), can help to set down the production scheduling plan and support the important decision-making of the port. A simulation to the quay side is implemented by the AnyLogic 6.2 and SQL Server 2005 and berth allocation based on practical data is given emphasis to discuss. The simulation result precedes the quondam research and actual data to 5.14% and 26.82% through the dynamic priority scheduling. In addition, a series of similar simulations are executed to support the decision on the proportion between berth and quay crane. All verify the feasibility and creditability of the above idea and the effective scheduling strategy in the framework will be further discussed and studied when the container terminal is provided with the multifarious characteristics in the future.

Generating scenarios for simulation and optimization of container terminal logistics

This paper introduces an approach for generating scenarios of sea port container terminals. A scenario consists of arrivals of deep sea vessels, feeder ships, trains, and trucks together with lists of containers to be loaded and unloaded. Moreover, container attributes such as size, empty, reefer, weight, and destination are included. The generator is based on a large number of parameters that allow the user to produce realistic scenarios of any size. The purpose of this paper is to outline the parameters that are important to produce realistic scenarios of high practical relevance and to propose an algorithm that computes scenarios on the basis of these parameters. The scenarios can be used as input data for simulation models. Furthermore, they can be employed as test data for algorithms to solve optimization problems in container terminal logistics such as berth planning and crane scheduling. The generator discussed here has been developed within the simulation project at the HHLA Container-Terminal Altenwerder in Hamburg, Germany. Nevertheless, its structure is general enough to be applied to various other terminals as well.

A general framework for scheduling equipment and manpower at container terminals

In this paper, we propose a general model for various scheduling problems that occur in container terminal logistics. The scheduling model consists of the assignment of jobs to resources and the temporal arrangement of the jobs subject to precedence constraints and sequence-dependent setup times. We demonstrate how the model can be applied to solve several different real-world problems from container terminals in the port of Hamburg (Germany). We consider scheduling problems for straddle carriers, automated guided vehicles (AGVs), stacking cranes, and workers who handle reefer containers. Subsequently, we discuss priority rule based heuristics as well as a genetic algorithm for the general model. Based on a tailored generator for experimental data, we examine the performance of the heuristics in a computational study. We obtain promising results that suggest that the genetic algorithm is well suited for application in practice.