# Real-Time Monitoring and Optimal Resource Allocation for Automated Container Terminals: A Digital Twin Application at the Yangshan Port

Authors: Yi Ding; Zhichao Zhang; Kaimin Chen; Haoyi Ding; Stefan Voss; Leonard Heilig; Yue Chen; Xiazhong Chen
Journal: Journal of Advanced Transportation
 (2023)

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## Abstract

Digital twins can facilitate high-fidelity representations of container terminals by applying various technologies and methods to better measure, understand, and improve operations. In this paper, a decision support system (DSS) based on digital twin and big data technologies is designed to demonstrate how real-time monitoring and an integrated decision support can be established. The DSS provides optimal operation plans and the benchmark for vessel delay early warnings through different resource allocation simulations at the planning level. It further enables real-time operational decision making through real-time monitoring and efficiency analyses using big data engines at the operational level. A case study is conducted for the ultralarge Yangshan Deepwater Automated Container Terminal Phase IV (ACT4) in Shanghai (China) and experimental results have revealed that the proposed digital twin-based DSS can help ACT4 operators to evaluate vessel service using optimized resource allocation plans and operations.


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## Body

## 1. Introduction

Container terminals (CTs) serve as the primary interface for cargo flows between land and sea and play a significant role in promoting global trade. Due to ever-increasing vessel sizes, container volumes, and limited resources, improving the operational efficiency of container terminals to serve vessels in time and stay competitive remains a pressing issue for global port operators. As a result, in the last decades, researchers have focused on developing methods to improve the utilization of terminal resources allocation [1, 2] in the terms of berths and quay cranes (QCs) [3, 4], storage yards [5], and yard trucks (YTs) [6]. Most of the aforementioned studies employed mathematical models to solve decision support problems. However, these models fail to consider operational factors and variables affecting on-going operations in terminals, which limit their practical applicability. In real-world operational scenarios, various factors cannot be considered by mathematical models in advance, such as YT transportation blocking, equipment breakdowns, inaccurate container information, and weather impacts. Accordingly, studies have applied simulation models to evaluate the performance of mathematical models under uncertain scenarios in order to overcome the shortcomings of traditional optimization models. However, one major challenge faced by these approaches is that they are again building on many static assumptions and cannot fully reflect the real-time situation [7]. As a consequence, these methods often do not allow operators to make real-time decisions on changing existing operational plans when uncertainties arise.Digital twins (DTs) facilitate a virtual representation of physical systems and their associated processes and environments by establishing a bidirectional real-time exchange of information [8]. Recently, DTs have been highlighted as one of the most important areas for terminal operations by major container terminal operators. Many works have emphasized the importance of DTs for decision making in the context of container terminals [9–11], while the practical application is still scarce. In related studies, a DT is often used as a digital representation of the real-world system for simulation purposes in order to verify that optimization results are applicable in a more complex environment. Moreover, the application of DTs in container terminals implies that massive amounts of data need to be handled. To virtually represent physical terminal infrastructure and equipment, the collection of data is usually established by adopting IoT (Internet of Things) technologies, such as sensors and actuators. Automated equipment, such as automated guided vehicles (AGVs), is managed by equipment control systems (ECS), which provide direct access to machinery data. To represent the processes and environment, additional data sources can be integrated into the DT, such as transactional data from the terminal operating system (TOS) or predictions of vessel ETAs (estimated time of arrival) and weather from external services. Therefore, the application of DTs in CTs is highly reliant on big data technologies to allow real-time decision making.The Shanghai Yangshan Deepwater Automated Container Terminal Phase IV (ACT4) is the world’s largest automated terminal [12] and is regarded highly in the CT industry. Owing to its size and reputation, ACT4 contributes toward the development of the Shanghai-Yangshan multilayered gateway hub as well as Shanghai’s status as a global city [13]. Since its trial run in December 2017, the ACT4 has accumulated massive amounts of data pertaining to equipment performance and operational processes. Given the amount of data, there is an untapped potential in better understanding and improving terminal operations using a DT. However, the processing of big data is a challenge in terms of collecting, cleaning, and mining massive datasets, as well as managing the increasingly cumbersome data-processing pipelines [14].To overcome the shortage of traditional optimization models and simulation models, we propose a novel real-time DT-based DSS framework which constantly monitors current operations, triggers planning processes, and simulates future scenarios using adjusted parameters in order to adapt related operational plans and processes. In this respect, different operational macrolevel planning problems and the microlevel real-time operation monitoring are considered for optimizing the resource utilization for berths, QCs, YTs, and yard space. Also, we design a feedback mechanism based on early warning of vessel delays to integrate macrolevel resource planning and microlevel operations. This integration is essential for the efficient operation of a DT-based DSS system. First of all, the DSS not only simulates macrolevel planning but also provides real-time monitoring and decision support through drill-down analysis of operating entities at the microlevel. Moreover, it can further optimize the plan at the macrolevel by adjusting the parameters at the microlevel. As a first application, we consider vessel service delays early warning, which cannot only lead to a lower customer satisfaction and penalties but also to reflect macroplanning decisions to the microentity of the vessel. For this purpose, the DSS simultaneously implements a resource allocation plan for the next 24 hours of operations, uses the optimization results to simulate the next 8 hours of operations, and selects the best resource allocation plan based on a feedback mechanism. When the terminal is in operation, the DT-based DSS not only monitors the entire process in real-time but also provides early warnings and optimized plans to the operator.Our study contributes toward existing literature as follows: First, the proposed DT-based DSS provides a full life-cycle resource optimization solution for terminals, from macrolevel resource planning to microlevel operations. Second, the feedback mechanism based on an early warning, integrating macrolevel resource planning and microlevel operations, can adjust the resource allocation plan in real-time through the DT to avoid vessel operation delays. Finally, a case demonstrates that our DSS framework extends the application of DT in an ultralarge CT and shows how a DT-based DSS can provide decision support for CT operators.The remainder of this paper is organized as follows: Section2 presents a literature review highlighting the motivations for the proposed DSS. Section 3 describes the architecture of DT for automated terminals. Section 4 further explains the design and implementation of the proposed system. Section 5 presents numerical experiments on the simulation results of the DSS, and analyses the effectiveness of real-time monitoring and early-warning mechanism for certain cases. Section 6 presents the conclusions of this work and its limitations, along with the directions for future research.


## 2. Related Work

Since the 1960s, the development and use of information systems and technologies have significantly contributed towards the efficient operations in modern CTs [15]. In the current phase of digital transformation, maritime ports and terminals aim to establish smart procedures, involving the application of modern technologies to better measure, analyze, decide, and act in port operations. This involves concepts like IoT, big data, cloud computing, and blockchain, as well as tools to support (real-time) data visualization, analysis, and decision-making [16]. DTs combine several concepts, technologies and methods in order to establish a virtual twin, which is used to learn from past or simulated future scenarios in order to support or even make data-driven decisions that are applied in practice. Since the concept of DTs was proposed by Michael Grieves in 2002 and later used by NASA in 2012 to describe the simulation model of complex parts [17], several studies have focused on their applications in various areas, including smart manufacturing [18], maintenance [19], supply chain management [20], production logistics [21], and construction site logistics [22]. In this section, we specifically review literature proposing DTs as a basis for decision support in container terminals.The application of DTs in container terminal is discussed on different levels in literature. In the term of macrolevel applications, the applications of DT technology in the maritime industry were compared with the manufacturing domain regarding conceptual and implementation frameworks, platforms [23], and real-world implementations in Taylor. Their study listed several DT platforms applied to maritime transport and confirmed the viability of applying DT systems in building of smarter terminals. Hofmann and Branding proposed a DT system, coupled with an IoT-platform, for optimizing dispatching policies using simulation-based performance forecasts in terminals [24]. Li et al. developed an innovative decision-making framework for capacity planning at mega CTs in Singapore that integrated DTs and advanced simulation-based optimization techniques [25]. However, their system is not strictly a DT because the system lacks the feedback mechanism to optimize handling operations in real-time based on simulation results. In contrast to the simulation-based optimization mode, Zhou et al. proposed a DT mode with optimization-embedded simulations for the maritime industry [26]. In this mode, once the optimization is completed, the output is sent back to the simulation model to update the parameters; thereafter, the simulation is resumed. This enabled the provision of real-time decision support to port operators. Using this mode, Zhou et al. developed a DSS with DT-based resilience analysis as an efficient tool for port resilience computation and updating. In the term of microlevel applications, Gao et al. present a DT-enabled automated storage yard scheduling framework and demonstrate its application for AGVs, automated stacking cranes (ASCs) and the storage area by minimizing the make span for all container-related tasks under dynamic/uncertain arrivals, showing that waiting times of ACSs can be reduced by adapting the number of used ACSs [27]. A similar approach is proposed by Li, describing a DT-based optimization the path finding for AGVs [28]. Ni proposed a DT-based approach for real-time completion of missing truck trajectory using neural networks [29].Overall, the above works are all useful applications of a DT at the macro or microlevel. The application of DTs at the macro level is conducive to the medium or long-term decision-making of the CT, while the application at the microlevel is to the real-time decision-making of the CT. However, there is no work applying DT to combine macrolevel resource planning and microlevel operations in container terminals. The combination of macro and microlevels can be used to verify the medium or long-term plan in real-time operations, and to further adjust the next macro plan using the validation results. To the best of our knowledge, our study is the first approach combining the macrolevel and microlevel in container terminals for decision support in the maritime industry, especially in container terminals. We developed a DT-embedded DSS to provide an optimal resource allocation plan and simulate it in operation of the DT. Simultaneously, its early warning function further serves as a critical decision support tool that helps terminal operators identify operational problems through real-time operational data analyses for avoiding vessel service delays.


## 3. Digital Twin for Automated Terminals

As a basis for real-time decision support in automated terminals, a digital twin has been developed and installed for the Phase 4 Automated Container Terminal (ACT4) at the Yangshan Port (Shanghai, China). Before presenting the real-time decision support system, which is embedded in the proposed digital twin, this section explains the main layers of the digital twin architecture, shown in Figure1. The architecture follows a common understanding from definitions (see, e.g., [8]) that a digital twin at least consists of a physical layer, a data layer, models representing the twinned objects and processes, a virtual layer, and decision support layer. This builds a foundation for advanced data analytics and decision support.Figure 1
Architecture of the digital twins.


### 3.1. Physical Layer

To facilitate a virtual representation of physical systems and their environment, the physical infrastructure and superstructure of ports and container terminals needs to be equipped with technologies allowing a constant collection of data about the physical entities. For infrastructure and manual equipment, this can be achieved by adopting IoT devices, such as sensors and actuators and by connecting to the programmable logic controller (PLC) of the machine. Edge devices facilitate a preprocessing of data and are connected to the data layer, usually by using data brokers. Automated equipment comes with an equipment control system (ECS) facilitating the data collection. The physical layer represents the terminal’s layout and configuration in terms of equipment in accordance with the port’s physical infrastructure.


### 3.2. Data Layer

The data layer facilitates data storage and the integration of contextual information in a digital twin. Additional data sources are integrated to enrich the real-timetime-series data from the physical layer with procedural (i.e., operational) data. These operational data are commonly stored in a terminal operating system (TOS). This allows not only a virtual representation of the infrastructure and physical equipment with contextual information but also provides the foundation for advanced data analytics and decision support. Therefore, the data layer is usually implemented as a data lake with several stages of data processing, organized with ETL (extract, transform, and load) processes in order to transform raw data into enriched data collections. For processing the large amounts of complex data, big data technologies are usually required.


### 3.3. Model Layer

Each digital twin contains a model of unspecified complexity to ensure a representative behavior of the twinned physical system or operation. This is required to analyse the system’s behavior under different circumstances. As the data layer allows a highly accurate capturing of terminal operations, models need to be able to represent detailed information. For this purpose, simulation models are oftentimes used as a basis and, in some cases, combined with mathematical models. In this work, we use discrete-event simulation (DES) models to simulate the prospective operational status in order to establish an early-warning system for vessel handling delays.We divide the simulation model into the following parts: (1) the vessel simulator represents vessel arrivals at the terminal. For our computational experiments, we use a k-order Erlang distribution to represent the time interval between the independent random events. Note that this can be easily replaced by real data from the TOS. A vessel entry provides information about the estimated arrival time, planned departure time as well as loading and unloading container volumes. (2) Quay area simulator: once a vessel is berthed, the quay area simulator is executed by scheduling QC load and unload procedures. (3) The yard simulator simulates container transports by yard trucks (YT) and yard cranes (YCs) taking into account the yard infrastructure, represented by yard blocks and transfer areas. (4) The gate simulator for simulating arrivals and departures at the truck gate.In addition, many rule-based policies of container terminals are encapsulated in the simulation model. For instance, a FCFS rule to handle all requests in the order they arrive is one of the most fundamental and widely used policies for operations. That is, it assumes that all requests have the same priority and is widely applied, such as for berthing management, QCs work plans and YCs scheduling plans. Also, a nearest neighbor (NN) policy defines the urgency of handling requests by the available equipment (QC, YT, and YC).


### 3.4. Virtual Layer

The virtual representation of twinned entities and processes is essential in most digital twin applications. On the virtual layer of our digital twin, we visualize the terminal layout and all operational entities. By interacting with the data layer, the virtual layer maps the entire life cycle of operational entities. This means that it not only maps the machinery data and operational status in real-time but also allows to backtrack all recorded historical operations. Figure2 shows a general 3D visualization of the ACT4. In particular, the interaction between different machines can be mapped in the virtual layer through the contextual information of the data layer. For instance, when a crane is in operation, the virtual layer can simultaneously visualize the YT, TC, and yard serving it. Moreover, the virtual layer is used to monitor equipment and operations. Based on the simulation model and decision support layer, it can be further used to represent what-if simulations for past and future scenarios.Figure 2
3D visualization of the ACT4 at the Yangshan Port (Shanghai).


### 3.5. Decision Support Layer

The decision support layer can be divided into two main modules, i.e., early-warning assessments and feedback loops. For the early-warning assessments, the main implementation technology is the big data distributed computing engine Spark which is adopted to calculate the results of the simulation and real-time streaming operation data, and evaluates the warning level. For the feedback loops, the main implementation technology is simulation-optimized model which is use to iteratively optimize coupling simulation models of resource allocation plans outputting from the model layer; the technical implementation details are illustrated in Section4.3.2. In the proposed digital twin, we embed a decision support system, further explained in the next section, which is based on discrete-event simulations and combinatorial optimization.


## 3.1. Physical Layer

To facilitate a virtual representation of physical systems and their environment, the physical infrastructure and superstructure of ports and container terminals needs to be equipped with technologies allowing a constant collection of data about the physical entities. For infrastructure and manual equipment, this can be achieved by adopting IoT devices, such as sensors and actuators and by connecting to the programmable logic controller (PLC) of the machine. Edge devices facilitate a preprocessing of data and are connected to the data layer, usually by using data brokers. Automated equipment comes with an equipment control system (ECS) facilitating the data collection. The physical layer represents the terminal’s layout and configuration in terms of equipment in accordance with the port’s physical infrastructure.


## 3.2. Data Layer

The data layer facilitates data storage and the integration of contextual information in a digital twin. Additional data sources are integrated to enrich the real-timetime-series data from the physical layer with procedural (i.e., operational) data. These operational data are commonly stored in a terminal operating system (TOS). This allows not only a virtual representation of the infrastructure and physical equipment with contextual information but also provides the foundation for advanced data analytics and decision support. Therefore, the data layer is usually implemented as a data lake with several stages of data processing, organized with ETL (extract, transform, and load) processes in order to transform raw data into enriched data collections. For processing the large amounts of complex data, big data technologies are usually required.


## 3.3. Model Layer

Each digital twin contains a model of unspecified complexity to ensure a representative behavior of the twinned physical system or operation. This is required to analyse the system’s behavior under different circumstances. As the data layer allows a highly accurate capturing of terminal operations, models need to be able to represent detailed information. For this purpose, simulation models are oftentimes used as a basis and, in some cases, combined with mathematical models. In this work, we use discrete-event simulation (DES) models to simulate the prospective operational status in order to establish an early-warning system for vessel handling delays.We divide the simulation model into the following parts: (1) the vessel simulator represents vessel arrivals at the terminal. For our computational experiments, we use a k-order Erlang distribution to represent the time interval between the independent random events. Note that this can be easily replaced by real data from the TOS. A vessel entry provides information about the estimated arrival time, planned departure time as well as loading and unloading container volumes. (2) Quay area simulator: once a vessel is berthed, the quay area simulator is executed by scheduling QC load and unload procedures. (3) The yard simulator simulates container transports by yard trucks (YT) and yard cranes (YCs) taking into account the yard infrastructure, represented by yard blocks and transfer areas. (4) The gate simulator for simulating arrivals and departures at the truck gate.In addition, many rule-based policies of container terminals are encapsulated in the simulation model. For instance, a FCFS rule to handle all requests in the order they arrive is one of the most fundamental and widely used policies for operations. That is, it assumes that all requests have the same priority and is widely applied, such as for berthing management, QCs work plans and YCs scheduling plans. Also, a nearest neighbor (NN) policy defines the urgency of handling requests by the available equipment (QC, YT, and YC).


## 3.4. Virtual Layer

The virtual representation of twinned entities and processes is essential in most digital twin applications. On the virtual layer of our digital twin, we visualize the terminal layout and all operational entities. By interacting with the data layer, the virtual layer maps the entire life cycle of operational entities. This means that it not only maps the machinery data and operational status in real-time but also allows to backtrack all recorded historical operations. Figure2 shows a general 3D visualization of the ACT4. In particular, the interaction between different machines can be mapped in the virtual layer through the contextual information of the data layer. For instance, when a crane is in operation, the virtual layer can simultaneously visualize the YT, TC, and yard serving it. Moreover, the virtual layer is used to monitor equipment and operations. Based on the simulation model and decision support layer, it can be further used to represent what-if simulations for past and future scenarios.Figure 2
3D visualization of the ACT4 at the Yangshan Port (Shanghai).


## 3.5. Decision Support Layer

The decision support layer can be divided into two main modules, i.e., early-warning assessments and feedback loops. For the early-warning assessments, the main implementation technology is the big data distributed computing engine Spark which is adopted to calculate the results of the simulation and real-time streaming operation data, and evaluates the warning level. For the feedback loops, the main implementation technology is simulation-optimized model which is use to iteratively optimize coupling simulation models of resource allocation plans outputting from the model layer; the technical implementation details are illustrated in Section4.3.2. In the proposed digital twin, we embed a decision support system, further explained in the next section, which is based on discrete-event simulations and combinatorial optimization.


## 4. Real-Time Decision Support System

The DT-based DSS framework is shown in Figure3. In this framework, the DT-embedded DSS has the following three levels: planning, operation, and analysis levels. At the planning level, the principal objective is to make plans before the operation starts and to optimize the resource allocation and the output of the optimizer is used as the input parameters for the simulation of future events in the DT by discrete-event simulation (DES) models. At the operation level, the output of the planning level would be used as the input parameters to simulate and discrete-event-based simulation models would simulate future events in a DT. Moreover, the DT embedded in this layer would monitor the operation status of the current port through real-time interaction of physical and virtual layers. At the analysis level, the simulation results are evaluated to determine whether the convergence conditions are satisfied. Then, the feedback mechanism is rescheduled at the planning level until iterative termination. The final output of the simulation serves as a benchmark to assess the early-warning level for decision support.Figure 3
DSS framework-based digital twins for ACT4.The main advantage of the DT-based DSS framework is that it can provide comprehensive real-time decision support from the macroplanning and microoperation levels. Besides, the early-warning-based feedback mechanism connects the macro and microlevels. In general, the application of DSS framework can be divided two aspects. In the planning phase, the proposed DSS framework can provide operators the optimal allocation planning of terminal resources in advance using DT-based simulation. During the operation phase, it can provide real-time monitoring and early warning based on the simulation results.


### 4.1. Planning Level

The complexity of port operations requires operators to make resource allocation plan in advance. Specifically, the domains of the resource allocation plan include the berth allocation and quay crane allocation plan (BACAP) [3, 30], quay crane scheduling plan (QCSP) [4], yard truck schedule plan (YTSP) [5] and yard crane scheduling plan (YCSP) [6] at the planning level of our DSS framework. The BACAP allocates the seaside resources; more specifically, it determines when and where vessels should berth, service hours, and QC resources for each vessel. The QCSP also determines the QC working sequence during loading or unloading. The YT, also known as the automated guided vehicle (AGV) in automatic CTs, is a crucial resource for transport between the seaside and yard under the YTSP, which sets the YT working sequence. The YCSP simultaneously allocates the yard crane (YC) resources in the yard block.


### 4.2. Operation Level

At the operation level of the DT-based DSS framework for ACT4, the embedded DT describes the complex operational process, closely mirrors the real scenarios, and simulates discrete port events. Although many software products provide multifidelity simulation and rich component libraries, they lack a unified data interface of the port and a high coupling optimization algorithm based on the port. Therefore, we developed the DT-based DSS framework that integrates several advanced technologies, such as the game engine Unity and distributed big data engine Spark, to embed into our DT [31]. Figure 1 shows that its structure can be divided into the following four layers: physical, data, model, and virtual layers.Three types of port operations are monitored and simulated at the operational level of the DSS: imports, exports, and transhipments. Figure4 presents a simplified description of the port operations. An imported container is subjected to the following process: When a vessel arrives at the port, the designated berth and QC resources are allocated to this vessel in advance according to the BACAP. When port operations are underway, the imported container is unloaded by the QC from the seaside to the landside, where it waits for the arrival of a preassigned YT for retrieval. The destination of the YT at the yard block is set by the YTSP. Shortly after the YT arrives at the yard, the container is lifted by a seaside YC for storage in the yard block. When a truck dispatched by the cargo owner enters the port through the gate, the container is picked up by the landside YC from the yard block and brought to the truck. Finally, the truck leaves the container port. The handling of export containers follows the same process but in reverse.Figure 4
Container port operation plan.


### 4.3. Analysis Level

Based on the results of the DT simulation, the analysis level can be divided into the following two main modules: the early-warning assessment and feedback mechanism. For each simulation scenario in the DT, we used the QC utilization rate to evaluate the simulation results. Then, whether the simulation should be repeated was evaluated. Afterwards, the early warning of a vessel operation delay (VOD) is revoked by the DSS based on the simulation results while it monitors port operations.


#### 4.3.1. Vessel Operation Delay Early Warning

Vessel service quality is the most significant indicator of CT operations. Thus, the purpose of optimizing resource allocation is to improve the vessel service quality. In a real operation scenario, although operators have completed the berth allocation plan that sets the berthing position and servicing of the vessel hours before its arrival, many uncertain factors exist in port operations. In other words, the actual operating times of vessels often deviate from the estimated operating times. For instance, owing to unexpected QC damage, the actual operating time of vessels may be longer than the planned operating time. In general, VODs are caused by QC operations. Although other operations may cause delays, these will eventually be attributed to the QCs. Thus, this study quantifies the VOD early warning with the QC utilization rate [32]. This was determined using the parameters returned to the simulation results in the DT. This implies that, at the operation level of the DSS, the QC utilization rate Ui is calculated with the output of the simulation in the DT.Figure5 shows how the VOD early-warning returned with the DSS. In this flowchart, the vessel simulator was simulated after inputting the parameters and real-time operation data at the planning level. Then, vessels and QCs were generated by the vessel simulator and QC simulator. In addition, the optimizer was triggered while the simulation was running in the DT. Until the best plan was chosen according to the feedback mechanism, its QC utilization rate was returned. Specifically, equation (1) indicates that QC resources are allocated to each vessel i in the simulation, where Ωqi represents the average number of QCs used by vessel i during the simulation, and τi,wk is the maximum QC operation time required by vessel i in the simulation. Equation (2) indicates the QC utilization rate of vessel i, which represents the ratio of the estimated QC resources qi and the QC resources in simulation Ci. While port operations are underway, the DT would monitor and calculate the real-time QC utilization rate U˜i by using the big data engine embedded in the DT. Then, the VOD early warning is returned to provide decision support. Equation (3) indicates the VOD early-warning level, which is the ratio of Ui to U˜i. If the VOD level is too high, that is, the real-time QC utilization rate U˜i is much less than the output Ui of the simulation in the DT, then the early warning will be sent with the DSS.(1)Ci=Ωqi∗τi,wk60,(2)Ui=qiCi=qi∗60Ωqi∗τi,wk,(3)η=UiU˜i.Figure 5
Vessel operation delay (VOD) early warning in analysis level flow.


#### 4.3.2. Feedback Mechanism

As shown in Figure3, BACAP, the first resource allocation plan at the planning level, requires a basic input parameter QC utilization. After the initial resource allocation plan is simulated in DT, the simulation results of the QC utilization Ui are feedback to planning level as BACAP input parameter and then this process iterates until the convergence condition is satisfied. If the convergence criterion is not satisfied, then the allocation plan for the berth and QC resources will be adjusted based on the feedback mechanism. Conversely, if the convergence criterion satisfies a predetermined threshold, then the simulation of the current time period is stopped, and the simulation results of the QC utilization Ui are returned. In this study, the following two convergence conditions were considered: the service time deviation and the QC resource allocation deviation per unit time. Equation (4) shows the cumulative service time deviation for all vessels V, where ei and ei′ are the departure times of vessel i before and after feedback correction, respectively; si is the time at which vessel i starts service. Equation (5) presents the cumulative QC resource allocation deviation per unit time for all vessels V, where qit and qit′ are the number of QCs required by vessel i before and after feedback correction in time period t, respectively.(4)α1=∑i∈Vei′−ei∑i∈Vei−si,(5)α2=∑t∈T∑i∈Vqit′−∑i∈Vqit∑t∈T∑i∈Vqit.To increase the convergence speed in the feedback correction process, only the vessel setV′, which satisfies one of the following two conditions, can be calculated: (i) equations (3) and (4) are not satisfied simultaneously and (ii) vessels still have conflicts after each iteration. Note that for each feedback iteration, only V′ needs to be calculated. This implies that these vessels do not satisfy the above conditions and must be rescheduled.


## 4.1. Planning Level

The complexity of port operations requires operators to make resource allocation plan in advance. Specifically, the domains of the resource allocation plan include the berth allocation and quay crane allocation plan (BACAP) [3, 30], quay crane scheduling plan (QCSP) [4], yard truck schedule plan (YTSP) [5] and yard crane scheduling plan (YCSP) [6] at the planning level of our DSS framework. The BACAP allocates the seaside resources; more specifically, it determines when and where vessels should berth, service hours, and QC resources for each vessel. The QCSP also determines the QC working sequence during loading or unloading. The YT, also known as the automated guided vehicle (AGV) in automatic CTs, is a crucial resource for transport between the seaside and yard under the YTSP, which sets the YT working sequence. The YCSP simultaneously allocates the yard crane (YC) resources in the yard block.


## 4.2. Operation Level

At the operation level of the DT-based DSS framework for ACT4, the embedded DT describes the complex operational process, closely mirrors the real scenarios, and simulates discrete port events. Although many software products provide multifidelity simulation and rich component libraries, they lack a unified data interface of the port and a high coupling optimization algorithm based on the port. Therefore, we developed the DT-based DSS framework that integrates several advanced technologies, such as the game engine Unity and distributed big data engine Spark, to embed into our DT [31]. Figure 1 shows that its structure can be divided into the following four layers: physical, data, model, and virtual layers.Three types of port operations are monitored and simulated at the operational level of the DSS: imports, exports, and transhipments. Figure4 presents a simplified description of the port operations. An imported container is subjected to the following process: When a vessel arrives at the port, the designated berth and QC resources are allocated to this vessel in advance according to the BACAP. When port operations are underway, the imported container is unloaded by the QC from the seaside to the landside, where it waits for the arrival of a preassigned YT for retrieval. The destination of the YT at the yard block is set by the YTSP. Shortly after the YT arrives at the yard, the container is lifted by a seaside YC for storage in the yard block. When a truck dispatched by the cargo owner enters the port through the gate, the container is picked up by the landside YC from the yard block and brought to the truck. Finally, the truck leaves the container port. The handling of export containers follows the same process but in reverse.Figure 4
Container port operation plan.


## 4.3. Analysis Level

Based on the results of the DT simulation, the analysis level can be divided into the following two main modules: the early-warning assessment and feedback mechanism. For each simulation scenario in the DT, we used the QC utilization rate to evaluate the simulation results. Then, whether the simulation should be repeated was evaluated. Afterwards, the early warning of a vessel operation delay (VOD) is revoked by the DSS based on the simulation results while it monitors port operations.


### 4.3.1. Vessel Operation Delay Early Warning

Vessel service quality is the most significant indicator of CT operations. Thus, the purpose of optimizing resource allocation is to improve the vessel service quality. In a real operation scenario, although operators have completed the berth allocation plan that sets the berthing position and servicing of the vessel hours before its arrival, many uncertain factors exist in port operations. In other words, the actual operating times of vessels often deviate from the estimated operating times. For instance, owing to unexpected QC damage, the actual operating time of vessels may be longer than the planned operating time. In general, VODs are caused by QC operations. Although other operations may cause delays, these will eventually be attributed to the QCs. Thus, this study quantifies the VOD early warning with the QC utilization rate [32]. This was determined using the parameters returned to the simulation results in the DT. This implies that, at the operation level of the DSS, the QC utilization rate Ui is calculated with the output of the simulation in the DT.Figure5 shows how the VOD early-warning returned with the DSS. In this flowchart, the vessel simulator was simulated after inputting the parameters and real-time operation data at the planning level. Then, vessels and QCs were generated by the vessel simulator and QC simulator. In addition, the optimizer was triggered while the simulation was running in the DT. Until the best plan was chosen according to the feedback mechanism, its QC utilization rate was returned. Specifically, equation (1) indicates that QC resources are allocated to each vessel i in the simulation, where Ωqi represents the average number of QCs used by vessel i during the simulation, and τi,wk is the maximum QC operation time required by vessel i in the simulation. Equation (2) indicates the QC utilization rate of vessel i, which represents the ratio of the estimated QC resources qi and the QC resources in simulation Ci. While port operations are underway, the DT would monitor and calculate the real-time QC utilization rate U˜i by using the big data engine embedded in the DT. Then, the VOD early warning is returned to provide decision support. Equation (3) indicates the VOD early-warning level, which is the ratio of Ui to U˜i. If the VOD level is too high, that is, the real-time QC utilization rate U˜i is much less than the output Ui of the simulation in the DT, then the early warning will be sent with the DSS.(1)Ci=Ωqi∗τi,wk60,(2)Ui=qiCi=qi∗60Ωqi∗τi,wk,(3)η=UiU˜i.Figure 5
Vessel operation delay (VOD) early warning in analysis level flow.


### 4.3.2. Feedback Mechanism

As shown in Figure3, BACAP, the first resource allocation plan at the planning level, requires a basic input parameter QC utilization. After the initial resource allocation plan is simulated in DT, the simulation results of the QC utilization Ui are feedback to planning level as BACAP input parameter and then this process iterates until the convergence condition is satisfied. If the convergence criterion is not satisfied, then the allocation plan for the berth and QC resources will be adjusted based on the feedback mechanism. Conversely, if the convergence criterion satisfies a predetermined threshold, then the simulation of the current time period is stopped, and the simulation results of the QC utilization Ui are returned. In this study, the following two convergence conditions were considered: the service time deviation and the QC resource allocation deviation per unit time. Equation (4) shows the cumulative service time deviation for all vessels V, where ei and ei′ are the departure times of vessel i before and after feedback correction, respectively; si is the time at which vessel i starts service. Equation (5) presents the cumulative QC resource allocation deviation per unit time for all vessels V, where qit and qit′ are the number of QCs required by vessel i before and after feedback correction in time period t, respectively.(4)α1=∑i∈Vei′−ei∑i∈Vei−si,(5)α2=∑t∈T∑i∈Vqit′−∑i∈Vqit∑t∈T∑i∈Vqit.To increase the convergence speed in the feedback correction process, only the vessel setV′, which satisfies one of the following two conditions, can be calculated: (i) equations (3) and (4) are not satisfied simultaneously and (ii) vessels still have conflicts after each iteration. Note that for each feedback iteration, only V′ needs to be calculated. This implies that these vessels do not satisfy the above conditions and must be rescheduled.


## 4.3.1. Vessel Operation Delay Early Warning

Vessel service quality is the most significant indicator of CT operations. Thus, the purpose of optimizing resource allocation is to improve the vessel service quality. In a real operation scenario, although operators have completed the berth allocation plan that sets the berthing position and servicing of the vessel hours before its arrival, many uncertain factors exist in port operations. In other words, the actual operating times of vessels often deviate from the estimated operating times. For instance, owing to unexpected QC damage, the actual operating time of vessels may be longer than the planned operating time. In general, VODs are caused by QC operations. Although other operations may cause delays, these will eventually be attributed to the QCs. Thus, this study quantifies the VOD early warning with the QC utilization rate [32]. This was determined using the parameters returned to the simulation results in the DT. This implies that, at the operation level of the DSS, the QC utilization rate Ui is calculated with the output of the simulation in the DT.Figure5 shows how the VOD early-warning returned with the DSS. In this flowchart, the vessel simulator was simulated after inputting the parameters and real-time operation data at the planning level. Then, vessels and QCs were generated by the vessel simulator and QC simulator. In addition, the optimizer was triggered while the simulation was running in the DT. Until the best plan was chosen according to the feedback mechanism, its QC utilization rate was returned. Specifically, equation (1) indicates that QC resources are allocated to each vessel i in the simulation, where Ωqi represents the average number of QCs used by vessel i during the simulation, and τi,wk is the maximum QC operation time required by vessel i in the simulation. Equation (2) indicates the QC utilization rate of vessel i, which represents the ratio of the estimated QC resources qi and the QC resources in simulation Ci. While port operations are underway, the DT would monitor and calculate the real-time QC utilization rate U˜i by using the big data engine embedded in the DT. Then, the VOD early warning is returned to provide decision support. Equation (3) indicates the VOD early-warning level, which is the ratio of Ui to U˜i. If the VOD level is too high, that is, the real-time QC utilization rate U˜i is much less than the output Ui of the simulation in the DT, then the early warning will be sent with the DSS.(1)Ci=Ωqi∗τi,wk60,(2)Ui=qiCi=qi∗60Ωqi∗τi,wk,(3)η=UiU˜i.Figure 5
Vessel operation delay (VOD) early warning in analysis level flow.


## 4.3.2. Feedback Mechanism

As shown in Figure3, BACAP, the first resource allocation plan at the planning level, requires a basic input parameter QC utilization. After the initial resource allocation plan is simulated in DT, the simulation results of the QC utilization Ui are feedback to planning level as BACAP input parameter and then this process iterates until the convergence condition is satisfied. If the convergence criterion is not satisfied, then the allocation plan for the berth and QC resources will be adjusted based on the feedback mechanism. Conversely, if the convergence criterion satisfies a predetermined threshold, then the simulation of the current time period is stopped, and the simulation results of the QC utilization Ui are returned. In this study, the following two convergence conditions were considered: the service time deviation and the QC resource allocation deviation per unit time. Equation (4) shows the cumulative service time deviation for all vessels V, where ei and ei′ are the departure times of vessel i before and after feedback correction, respectively; si is the time at which vessel i starts service. Equation (5) presents the cumulative QC resource allocation deviation per unit time for all vessels V, where qit and qit′ are the number of QCs required by vessel i before and after feedback correction in time period t, respectively.(4)α1=∑i∈Vei′−ei∑i∈Vei−si,(5)α2=∑t∈T∑i∈Vqit′−∑i∈Vqit∑t∈T∑i∈Vqit.To increase the convergence speed in the feedback correction process, only the vessel setV′, which satisfies one of the following two conditions, can be calculated: (i) equations (3) and (4) are not satisfied simultaneously and (ii) vessels still have conflicts after each iteration. Note that for each feedback iteration, only V′ needs to be calculated. This implies that these vessels do not satisfy the above conditions and must be rescheduled.


## 5. Case Study

In this section, the implementation of the DSS is presented. It is shown that our DT-based DSS can be implemented at the ACT4, the largest automated terminal in the world. A case study based on the data obtained from real operations is presented, which includes eight berths, 27 QCs, 200 YTs, and 117 ASCs being considered as physical infrastructure.


### 5.1. Optimal Allocation of Resources Based on DT

To obtain the final resource allocation strategy at the planning level of the DSS, we simulated the next 8 h of operation in the DT. Then, the results were evaluated at the analysis level and iterated on the parameters by a feedback mechanism. Table1 lists the operational data output at the planning level of the DSS, which represents the BACAP for the next 24 h. Note that the ESTi and EFTi indicate the earliest planned berthing time and the earliest planned departure time of the vessel, respectively.Table 1
BACAP of ACT4 in the next 24 h.
VesseIIDqiWorkloadbi0LengthQCmaxQCminESTiEFTi124.26310200513132379650250511123330.178527023041522411.42975001003119513.6355500110311524611.429762010031814Table2 presents the simulation results of the DT operation plan for the next 8 h, including the forward optimization process and the feedback control process. The upper limit of the number of feedback iterations is set as N=10 and number of replications are set as 1000. The convergence condition is α1≤5% and α2≤10%. It can be seen from Table 2 that the operation satisfies the convergence conditions in the eighth iteration. The QC utilization coefficient at this time is approximately 0.9. Figure 6 shows the trend of the average QC utilization rates and the standard deviation values in each iteration. Obviously, the standard deviation becomes smaller and tends to be stable as the number of iterations increases. At the same time, the eighth iteration is the maximum value. That is, the simulation result is the final operation plan for the next 8 h. This result can be considered as the benchmark to estimate the VOD early-warning level. When the real-time QC utilization rate of a certain vessel is significantly lower than the estimated value, the VOD early warning and supporting decision-making are triggered.Table 2
Simulation results for the next 8 h.
IterationsQC utilizationConvergenceU1U2U3U4U5U6α1 (%)α2 (%)11111118.35.920.820.830.750.770.740.809.57.130.780.780.790.800.780.789.07.340.810.750.820.780.820.817.44.750.830.880.820.840.790.817.74.960.880.810.790.820.860.848.65.770.860.830.810.810.830.877.15.380.910.900.910.910.920.893.22.590.900.900.920.890.890.893.93.1100.910.910.900.920.900.904.93.1Figure 6
QC average utilization of each iteration.


### 5.2. DT-BasedReal-Time Monitoring

When the ACT4 operations are processing, the DT restores its data and operational information with high-fidelity (see Figure2), including the operable monitoring of operating entities and the real-time indicator calculations based on the big data engine. As shown in Figure 7(a), the DSS shows that the truck has timed out waiting in the container area. And when we apply the associated button to display the entities that are associated with this container area, we can see that it is working for two vessels (vessel 1 and vessel 2) at the same time. It means that the unreasonable stacking plan in advance made the loading and unloading containers of the two vessels concentrated in one container area, resulting in a shortage of mechanical resources belonging to this container area and truck delays.Figure 7
(a) Real-time monitoring of trucks operating on the landside. (b) Container area correlation analysis.
(a)(b)


### 5.3. Efficiency Analysis Based on Early-Warning Mechanism

Through the planning layer, operation layer, and analysis layer, the DT-based DSS pushes the implementation of VOD early-warning to DT, so that operators can improve the process of monitoring. Based on this early-warning mechanism, the DT also supports a drill-down analysis, allowing operators to accurately locate bottlenecks.In Figure8(a), four vessels are operating under the DT’s real-time monitoring. As shown in Figure 8(b), the DSS issues a VOD early-warning that the vessel would be delayed. Table 3 shows the VOD early warning sent by the DSS at 15:00 on March 16, 2022. It can be seen that the VOD early-warning level of vessel 2 is too high and will be sent to the operator. In Figure 9, its QC utilization is lower than the estimated value according to the DT simulation results. In other words, this operating status is abnormal and could delay the departure of the vessel.Figure 8
(a) Real-time monitoring of ships operating on the seaside. (b) Real-time sending VOD early warning.
(a)(b)Table 3
VOD early-warning level of working vessels at 15:00 in March.
VesselVOD early-warning level10.8723.3831.0640.96Figure 9
Comparison of real-time QC utilization and simulation-estimated QC utilization.To further explore the reasons for the VOD early warning, we carried out a drill-down analysis of the QCs operating on the vessel 2. Figure10 shows the machine hour rate (MHR) and abnormal state of each QC. It can be seen from Figure 10 that the status of the QCs. Specially, QC No. 112 whose MHR is lower than others significantly.Figure 10
Machine hour rate of quay cranes related to the potentially delayed vessel YM MODERATION at 15:00 on March 16, 2022.To further analyze the abnormal operation of QC No. 112, Table4 shows the monitoring results of the operation efficiency indicators of the QC for approximately 1 h. In our DSS, the QC has the following three KPIs:(1)
YT driving time: driving time of YT to mission point(2)
YT number: the YT number assigned to the QC in a certain time period(3)
Working time: time spent on the current work instructionsTable 4
YT driving time, YT number, and working time of the current instruction of QC No. 112 at each point in time from 15:00 to 16:00 on March 16, 2022.
TimeYT driving timeYT numberWorking time15:0012.136.215:0515.323.215:1014.2111.115:1511.729.215:209.729.315:2516.138.115:3013.446.215:3515.844.215:4012.322.515:4511.54315:5015.632.515:5514.84316:0011.436.2Table4 shows three real-time KPIs within 1 h. The number of YT assigned to 112 QC is small. Often, a QC has 4 sets of YTs, but between 15:10 and 15:35, only 1 to 3 sets of YTs were assigned to QC 112. Therefore, the working time at 15:10 and 15:35 is too long, e.g., at 15:10, the working time is 11 minutes. Generally speaking, the working time is often 5 minutes. To avoid vessel operation delays, the operator can increase the number of YTs assigned to the QC in subsequent operations. Simultaneously, driving time of the YT is abnormal. This indicates that the berthing position of the vessel is far away from the storage yard, which means that the prior berthing plan is not reasonable. Thus, using DSS for the drill-down analysis of port operations, operators can be provided with decision support from the VOD early warning to the QC and YT.The results demonstrate that the application value of the DSS for operators is evident in two phases. In the planning phase, the DSS enables advanced resource allocation, which is then simulated in the DT to iterate the optimal allocation plan. Afterwards, it determines the benchmark for the VOD early warning. In the operation phase, the DT conducts real-time monitoring and calculates efficiency indicators through the big data engine. Once the vessel issues a delay warning based on the simulation results, the DSS will drill-down through the relevant KPIs to identify the cause.


## 5.1. Optimal Allocation of Resources Based on DT

To obtain the final resource allocation strategy at the planning level of the DSS, we simulated the next 8 h of operation in the DT. Then, the results were evaluated at the analysis level and iterated on the parameters by a feedback mechanism. Table1 lists the operational data output at the planning level of the DSS, which represents the BACAP for the next 24 h. Note that the ESTi and EFTi indicate the earliest planned berthing time and the earliest planned departure time of the vessel, respectively.Table 1
BACAP of ACT4 in the next 24 h.
VesseIIDqiWorkloadbi0LengthQCmaxQCminESTiEFTi124.26310200513132379650250511123330.178527023041522411.42975001003119513.6355500110311524611.429762010031814Table2 presents the simulation results of the DT operation plan for the next 8 h, including the forward optimization process and the feedback control process. The upper limit of the number of feedback iterations is set as N=10 and number of replications are set as 1000. The convergence condition is α1≤5% and α2≤10%. It can be seen from Table 2 that the operation satisfies the convergence conditions in the eighth iteration. The QC utilization coefficient at this time is approximately 0.9. Figure 6 shows the trend of the average QC utilization rates and the standard deviation values in each iteration. Obviously, the standard deviation becomes smaller and tends to be stable as the number of iterations increases. At the same time, the eighth iteration is the maximum value. That is, the simulation result is the final operation plan for the next 8 h. This result can be considered as the benchmark to estimate the VOD early-warning level. When the real-time QC utilization rate of a certain vessel is significantly lower than the estimated value, the VOD early warning and supporting decision-making are triggered.Table 2
Simulation results for the next 8 h.
IterationsQC utilizationConvergenceU1U2U3U4U5U6α1 (%)α2 (%)11111118.35.920.820.830.750.770.740.809.57.130.780.780.790.800.780.789.07.340.810.750.820.780.820.817.44.750.830.880.820.840.790.817.74.960.880.810.790.820.860.848.65.770.860.830.810.810.830.877.15.380.910.900.910.910.920.893.22.590.900.900.920.890.890.893.93.1100.910.910.900.920.900.904.93.1Figure 6
QC average utilization of each iteration.


## 5.2. DT-BasedReal-Time Monitoring

When the ACT4 operations are processing, the DT restores its data and operational information with high-fidelity (see Figure2), including the operable monitoring of operating entities and the real-time indicator calculations based on the big data engine. As shown in Figure 7(a), the DSS shows that the truck has timed out waiting in the container area. And when we apply the associated button to display the entities that are associated with this container area, we can see that it is working for two vessels (vessel 1 and vessel 2) at the same time. It means that the unreasonable stacking plan in advance made the loading and unloading containers of the two vessels concentrated in one container area, resulting in a shortage of mechanical resources belonging to this container area and truck delays.Figure 7
(a) Real-time monitoring of trucks operating on the landside. (b) Container area correlation analysis.
(a)(b)


## 5.3. Efficiency Analysis Based on Early-Warning Mechanism

Through the planning layer, operation layer, and analysis layer, the DT-based DSS pushes the implementation of VOD early-warning to DT, so that operators can improve the process of monitoring. Based on this early-warning mechanism, the DT also supports a drill-down analysis, allowing operators to accurately locate bottlenecks.In Figure8(a), four vessels are operating under the DT’s real-time monitoring. As shown in Figure 8(b), the DSS issues a VOD early-warning that the vessel would be delayed. Table 3 shows the VOD early warning sent by the DSS at 15:00 on March 16, 2022. It can be seen that the VOD early-warning level of vessel 2 is too high and will be sent to the operator. In Figure 9, its QC utilization is lower than the estimated value according to the DT simulation results. In other words, this operating status is abnormal and could delay the departure of the vessel.Figure 8
(a) Real-time monitoring of ships operating on the seaside. (b) Real-time sending VOD early warning.
(a)(b)Table 3
VOD early-warning level of working vessels at 15:00 in March.
VesselVOD early-warning level10.8723.3831.0640.96Figure 9
Comparison of real-time QC utilization and simulation-estimated QC utilization.To further explore the reasons for the VOD early warning, we carried out a drill-down analysis of the QCs operating on the vessel 2. Figure10 shows the machine hour rate (MHR) and abnormal state of each QC. It can be seen from Figure 10 that the status of the QCs. Specially, QC No. 112 whose MHR is lower than others significantly.Figure 10
Machine hour rate of quay cranes related to the potentially delayed vessel YM MODERATION at 15:00 on March 16, 2022.To further analyze the abnormal operation of QC No. 112, Table4 shows the monitoring results of the operation efficiency indicators of the QC for approximately 1 h. In our DSS, the QC has the following three KPIs:(1)
YT driving time: driving time of YT to mission point(2)
YT number: the YT number assigned to the QC in a certain time period(3)
Working time: time spent on the current work instructionsTable 4
YT driving time, YT number, and working time of the current instruction of QC No. 112 at each point in time from 15:00 to 16:00 on March 16, 2022.
TimeYT driving timeYT numberWorking time15:0012.136.215:0515.323.215:1014.2111.115:1511.729.215:209.729.315:2516.138.115:3013.446.215:3515.844.215:4012.322.515:4511.54315:5015.632.515:5514.84316:0011.436.2Table4 shows three real-time KPIs within 1 h. The number of YT assigned to 112 QC is small. Often, a QC has 4 sets of YTs, but between 15:10 and 15:35, only 1 to 3 sets of YTs were assigned to QC 112. Therefore, the working time at 15:10 and 15:35 is too long, e.g., at 15:10, the working time is 11 minutes. Generally speaking, the working time is often 5 minutes. To avoid vessel operation delays, the operator can increase the number of YTs assigned to the QC in subsequent operations. Simultaneously, driving time of the YT is abnormal. This indicates that the berthing position of the vessel is far away from the storage yard, which means that the prior berthing plan is not reasonable. Thus, using DSS for the drill-down analysis of port operations, operators can be provided with decision support from the VOD early warning to the QC and YT.The results demonstrate that the application value of the DSS for operators is evident in two phases. In the planning phase, the DSS enables advanced resource allocation, which is then simulated in the DT to iterate the optimal allocation plan. Afterwards, it determines the benchmark for the VOD early warning. In the operation phase, the DT conducts real-time monitoring and calculates efficiency indicators through the big data engine. Once the vessel issues a delay warning based on the simulation results, the DSS will drill-down through the relevant KPIs to identify the cause.


## 6. Conclusion

DT technology currently enjoys wide applications in container terminals. However, there are few research studies in CT applying DT to provide full life-cycle decision support from macrolevel to microlevel. For this purpose, this paper presents a DSS framework based on a DT and big data analysis to monitor CT operations and allocate port resources. The system combines several cutting-edge and popular technologies such as big data, DT, and DES and provides decision support at the planning level and operation level. It also provides constantly monitors current operations, triggers planning processes, and simulates future scenarios using adjusted parameters in order to adapt related operational plans and processes.Our work contributes to previous studies by providing specific solutions for DT applications within a port, which overcomes many of the disadvantages of traditional mathematical models. To deal with the challenge of using real-time performance data in traditional research, a big data engine was embedded in our DT system. Moreover, the DT was embedded with early-warning mechanisms to predict potential disruptions or delays based on the current operating status. In practice, the system accelerates the decision processes for the ACT4 and benefits the terminal overall. From an academic standpoint, the system motivates operators to conduct further research that combines AI and DTs, such as applying AI and operational algorithms. Such algorithms help operators make smarter, quicker, and more precise predictions for terminals. The data space mentioned above provides a foundation for simulation-based multiagent intelligence research and dynamic AGV scheduling.To extend this system, some directions for future research can be considered. The existing early-warning mechanism is insufficient. In future research, the early-warning mechanism of the DT should not only provide early warnings but also provides specific decision support schemes in the early-warning state. For example, when a ship delay warning is issued, the system can provide the operator with recovery strategies for decision support.



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*Source: 6909801-2023-03-21.xml*