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Design of digital twin applications in automated storage yard scheduling
Abstract
A digital twin-enabled automated storage yard scheduling framework for uncertain port dispatching is proposed in this paper. Digital twin technology is employed to establish the virtual yet realistic storage yard and the connection between them. In the proposed framework, disturbed scenarios during practical operation are monitored, and real-time data is visualized in the virtual space to adapt to the time-varying environment. The proposed framework focuses on the optimization of three main resources, viz. storage area, automated stacking cranes (ASCs), and automated guided vehicles (AGVs). In addition, three key technologies, the Internet of Things (IoT), virtual reality, and digital thread, are adopted to develop the proposed scheduling system. A case study of ASC rescheduling due to dynamic arrival is used to demonstrate the effectiveness of the proposed framework and the significance of obtaining uncertainties in port optimization. Sensitivity analysis is conducted to define the appropriate configuration required to handle all tasks. The results show that digital twin applications in automated storage yard scheduling help operators make optimization decisions.
... El estudio se fijó bajo el paradigma cualitativo a través de una investigación documental, con diseño bibliográfico descriptivo. Para alcanzar su propósito, la categoría analizada fue: "innovación en la optimización de procesos, gemelos digitales", sobre la base de los constructos ofrecidos por Qian et al. (2023), Vered y Elliott (2023), Joyanes (2021), Gao et al. (2022), Hyre et al. (2022), Schluse et al. (2018), entre otros. Dentro de los hallazgos obtenidos, se conciliaron el análisis y reflexión acerca de una serie de constructos teóricos y documentos científicos que discurren sobre las categorías propuestas por la investigación, ofreciendo, de esta forma, evidencia científica y empírica de la temática planteada. ...
... To achieve its purpose, the category analyzed was Innovation in process optimization. Digital twins, based on the constructs offered by Qian et al. (2023), Vered and Elliott (2023), Joyanes (2021), Gao et al. (2022), Hyre et al. (2022), Schluse et al. (2018), among others. Within the findings obtained, the analysis and reflection on a series of theoretical constructs and scientific documents that reflect on the categories proposed by the research were reconciled, thus offering scientific and empirical evidence of the subject matter. ...
... Preliminar a este hecho, estas tecnologías fueron discernidas en los espacios académicos para la gestión idónea de conocimientos derivado de la dimensión espiritual del docente, al apropiar nuevos sucesos científico-tecnológicos en el hecho educativo, cultivando, de esta forma, nuevas sociedades de conocimiento(Bucchiarone, 2022;García et al., 2022;Čech y Vosáhlo, 2022).En adición a estas afirmaciones, en esta representación digital, más allá de proporcionar realismo e innovación, convergen en ella tecnologías como realidad virtual, realidad aumentada, realidad mixta, IoT, big data, machine learning y computación en la nube(Gao et al., 2022;Quiñonez, 2019) para la gestión eficiente, efectiva, eficaz y pertinente de la información que suministra. ...
... • Technology: Gao et al. (2022) defines the digital thread as the core technology for enabling DTs; ...
... Other articles examined the digital thread from a manufacturing process perspective without focusing on a specific industry (Cogswell et al., 2022;Pang et al., 2021;Paramatmuni & Cogswell, 2023;Singh & Willcox, 2021), but instead applied the digital thread in a specific use case (David et al., 2021(David et al., , 2023Hedberg et al., 2020;Helu et al., 2017;Kwon et al., 2020;Liu et al., 2023;Wang et al., 2020;Yang et al., 2022). The digital thread as a support tool for DT has been studied in several articles within the manufacturing process (Gao et al., 2022;Huang et al., 2023;Jiang et al., 2023;Niu & Qin, 2021) and aeronautical / aerospace applications (Zhang et al., 2022). One article studied the use of digital threads for circular manufacturing (CM) implementation within the manufacturing process applications (Deng et al., 2021). ...
... Digital Thread Definition Definition Terminology Scope Purpose (Gao et al., 2022) The digital thread is the core technology to process data from different sources and complete the transmission and flow, aiming at realizing the connection and interaction among modules in the digital twin-enabled scheduling framework. ...
Business operations and supporting data analysis initiatives are impeded by the silos of data present within departments, systems, and business units. Consequently, the ability of managers and engineers to harness data for operational management and informed decision-making is curtailed. The rapid advancements in technology have revolutionized various aspects of product development, manufacturing, operations, and end-of-life treatment. One such transformative concept, the digital thread, has emerged as an important paradigm. It orchestrates the integration of information and data along the entire product lifecycle, spanning from initial design and engineering through production, maintenance, use, and eventual end of life. While the digital thread has garnered increasing attention within both the research community and industrial enterprises, there remains a notable lack of standardization concerning its utilization and applications. This comprehensive literature review aims to explore the role of the digital thread in manufacturing within the context of the product lifecycle. As a result, this review synthesizes insights into the technologies, roles, and functions of the digital thread throughout the product lifecycle. Furthermore, it proposes a structured framework designed to impart a standardized perspective of the digital thread’s relevance within the manufacturing product lifecycle. Ultimately, this framework is poised to serve as a guiding resource for practitioners and researchers in designing and implementing digital threads.
... Several interesting findings emerge from the selected publications. Notably, Gao, et al. [20] show that a digital twin of an automated warehouse helps operators make optimization decisions. In addition, an order picking system is one of the application cases that can greatly benefit from the potential of a digital twin [21]. ...
... Analysis of the simulation results shows that the models designed according to the proposed architecture accurately represent real-world operations [27] Serve as a 3D virtual model of a warehouse Simulation Decision support Evaluate different scenarios in terms of time and cost for the identification of storage areas [20] Optimize the planning of the storage area, AGVs, and ASCs Decision Proposes a scheduling plan for ASCs [29] Control the parameters of the automatic handling system to prevent the part from falling off the pallet ...
... In many cases, the data used are incomplete. For example, Gao, et al. [20] acknowledge that several pieces of information that can impact the process flow, such as bad weather and road congestion, are missing from their model. ...
... Automated container terminals (ACTs) are a typical example of smart ports, and the ACT operating environment has been improved through the application of smart equipment and systems such as automated guided vehicles (AGVs), automated stacking cranes (ASCs), and terminal operation systems (TOSs). It has been observed that automated container handling equipment does not possess sufficient capacity to flexibly adjust system operation in the face of disturbances such as weather conditions, traffic congestion, and equipment failure, which could affect the overall efficiency of the terminal, and technologies such as digital twins have been proposed as a solution to this problem [5]. The concept of green growth and green port policy was introduced to reduce the environmental impact of ports and promote economic development while ensuring climate and environmental sustainability [6]. ...
... They also demonstrate how the digital twin could be applied in ports, including the vessel, yard operations, hinterland transport, and equipment aspects such as lighting and monitoring container handling machinery [21]. Some research cases have analyzed the scheduling logic of ASC on digital twin models [5] and the optimization of the energy consumption of ASC by applying Q-learning [22]. ...
Background: To improve port productivity, safety, and sustainability, the use of information and communication technology is being promoted as a smart port. The utilization of a terminal operation system (TOS) is important for advanced port operations, and it is necessary to organize the issues and characteristics of the TOS. Methods: The characteristics of TOSs introduced in Japan and those widely introduced in Europe and Southeast Asia will be investigated and discussed according to the port management system in Japan. Results: Japanese TOSs are characterized by a lack of automated functions, such as ship loading plans, and by the fact that they are designed to allow the crane driver to select the order of operations, which may be attributed to a system wherein stakeholders are segmented and on-site decisions are emphasized. The promotion of smart ports in Japanese-style ports requires a system for information linkage between stakeholders. Conclusions: TOS capabilities for smart ports should be implemented according to the characteristics of port management in each region, and the studies conducted in this paper are useful in examining port system implementation strategies.
... To address the shortcoming of supervised learning of requiring large-scale training data, Vasanthan and Nguyen [48] introduced a digital twin of the vessel to generate enough training data to solve the vessel's routing problem, effectively detecting potential collisions. Gao et al. [49] developed a digital-twin-enabled automated storage yard scheduling framework for uncertain port dispatching. ...
... requiring large-scale training data, Vasanthan and Nguyen [48] introduced a digital twin of the vessel to generate enough training data to solve the vessel's routing problem, effectively detecting potential collisions. Gao et al. [49] developed a digital-twin-enabled automated storage yard scheduling framework for uncertain port dispatching. ...
Automated guided vehicle (AGV) scheduling and routing are critical factors affecting the operation efficiency and transportation cost of the automated container terminal (ACT). Searching for the optimal AGV scheduling and routing plan are effective and efficient ways to improve its efficiency and reduce its cost. However, uncertainties in the physical environment of ACT can make it challenging to determine the optimal scheduling and routing plan. This paper presents the digital-twin-driven AGV scheduling and routing framework, aiming to deal with uncertainties in ACT. By introducing the digital twin, uncertain factors can be detected and handled through the interaction and fusion of physical and virtual spaces. The improved artificial fish swarm algorithm Dijkstra (IAFSA-Dijkstra) is proposed for the optimal AGV scheduling and routing solution, which will be verified in the virtual space and further fed back to the real world to guide actual AGV transport. Then, a twin-data-driven conflict prediction method is proposed to predict potential conflicts by constantly comparing the differences between physical and virtual ACT. Further, a conflict resolution method based on the Yen algorithm is explored to resolve predicted conflicts and drive the evolution of the scheme. Case study examples show that the proposed method can effectively improve efficiency and reduce the cost of AGV scheduling and routing in ACT.
... The International Journal of Advanced Manufacturing Technology process and use the data from the industrial shop floor to make informed decisions, which places higher demands on the combination of production process data and the twin model [23]; the multi-dimensional, multi-directional and high real-time integration of the full-factor data model and the mirror model of the physical shop floor is core to achieve dynamic monitoring and intelligent decision-making. ...
Digital twins have attracted more and more attention in the past few years. To put digital twins into practice, a large number of modeling approaches have been proposed, vast amounts of data have been collected, and their accuracy has been improving. However, current research has paid insufficient attention to the multi-scale features of the shop floor, which hinders the effective application of the digital twin shop floor. To address the problem of how to achieve effective multi-level and multi-dimensional fusion of digital twin models with production process data, this paper first proposes a structured data modeling framework for sorting out all the production process data collected in real-time; and then proposes a multi-level fusion framework for supporting the fusion of real-time data and twin models from the unit level to the system level. The method judges the parsed received data streams through the full-factor semanticization framework, and at the same time fuses the parsed data streams with the constructed full-factor twin model from multiple dimensions and layers, forming a twin model fusion method with real-time data streams as the blood and twin model as the skeleton. Finally, the micro-assembly-based production shop environment is selected as a case study to verify the correctness and feasibility of the proposed data grooming framework, data, and model fusion method.
... Accordingly, with the continuous development of the digital twin, Tao et al. proposed a maturity model of the digital twin and classified the maturity of the digital twin into six levels: "imitating the real with the virtual (L0), reflecting the real with the virtual (L1), controlling the real with the virtual (L2), anticipating the real with the virtual (L3), optimizing the real with the virtual (L4), and coexisting the real with the virtual (L5)" [21]. However, in the L1 stage, the simple table data display can no longer meet the needs of today's production activities, and a more intuitive and comprehensive way of reflecting reality is needed [22] The digital twin needs to dynamically monitor the production process and use the data from the industrial shop floor to make informed decisions, which places higher demands on the combination of production process data and the twin model [23]; the multi-dimensional, multi-directional and high real-time integration of the full-factor data model and the mirror model of the physical shop floor is core to achieve dynamic monitoring ...
Digital twins have attracted more and more attention in the past few years. To put digital twins into practice, a large number of modeling approaches have been proposed, vast amounts of data have been collected, and their accuracy has been improving. However, current research has paid insufficient attention to the multi-scale features of the shop floor, which hinders the effective application of the digital twin shop floor. To address the problem of how to achieve effective multi-level and multi-dimensional fusion of digital twin models with production process data, this paper first proposes a structured data modeling framework for sorting out all the production process data collected in real-time; and then proposes a multi-level fusion framework for supporting the fusion of real-time data and twin models from the unit level to the system level. The method judges the parsed received data streams through the full-factor semanticization framework, and at the same time fuses the parsed data streams with the constructed full-factor twin model from multiple dimensions and layers, forming a twin model fusion method with real-time data streams as the blood and twin model as the skeleton. Finally, the micro-assembly-based production shop environment is selected as a case study to verify the correctness and feasibility of the proposed data grooming framework, data, and model fusion method.
... The construction of the digital twin smart port empowers port business through the detection of comprehensive port posture [92], loading/unloading facilities [47], port logistics, and transportation [93]. In addition, the digital twin empowers management [89], the advanced intelligent search realizes efficient data retrieval and targeted query. ...
Driven by the IoT technology and smart sensors development in Industry 5.0, the digital twin as an innovative information technology brings new opportunities and challenges for intelligent maritime logistics management. This paper tries to present a systematic review on digital twin-empowered smart maritime logistics management by employing a bibliometric analysis framework under the Industry 5.0 era. The 3372 related publications from the Web of Science database are collected as research samples from 2003 to 2023. Besides, the VosViewer is adopted to perform the co-word and network analysis by visualizing interactive collaborations of published literature. Specifically, more than 3,000 articles on maritime logistics were reviewed to determine the research trajectories and main themes through same-word study and co-citation analysis. Results show that most publications on maritime logistics management are concentrated in China and the United States, where maritime logistics is developing towards digitization and informatization. In particular, Sustainability, Maritime Policy & Management, and Journal of Marine Science and Engineering are the most important journals focusing on maritime logistics management. Moreover, we hope this review study serves as a future direction on digital twin-empowered smart maritime logistics management practices for both researchers and practitioners.
... As a result, BIM has been integrated with wireless sensor networks to develop a real-time active model, with the aim of applying DT technology in the design phase of the construction industry [18]. • Construction Phase: During the construction phase, DT is utilized to aid in several management activities such as construction safety management [19][20][21][22], uncertain yard scheduling [23], and quality management [24]. Several technologies are utilized in the construction industry to enhance productivity and quality control. ...
The digital twin (DT) represents a powerful tool for advancing construction industry to provide a cyber-physical integration that enables real-time monitoring of assets and activities and facilitates decision-making. Due to the inherent characteristics of the construction industry and the diverse possibilities with DT, proliferation of building digital twin (BDT) necessitates a comprehensive comprehension of its evolution and the creation of roadmaps. This paper aims to contribute to the formalization and standardization of BDT. It designs a novel assessment framework for the overall maturity measurement of existing BDT projects. The developed BDT maturity model incorporates a collective opinion generation paradigm based on a fairness-aware multiobjective optimization model to provide an expert-based evaluation system for evaluating the maturity of BDT projects. The effectiveness and feasibility of the proposed framework have been validated through a case study of an experimental BDT initiative. This paper establishes a generalizable framework for BDT maturity assessment that can offer insights into BDT maturity standards to construction practitioners to create effective strategies for the diffusion, development, and maturation of BDT.
... A deep learning-based framework providing a solution and unification of deep learning techniques" allowing hybrid parallelization and operating within an IoT architecture is proposed in [6]. A framework for automated scheduling in a storage yard, enhanced by digital twin technology, is presented in [7] to offer enhanced optimization capabilities, particularly when addressing disruptions. By harnessing digital twin technology, real-time information can be immediately acquired from the actual storage yard, allowing for iterative simulation-based optimization within the virtual yard. ...
The paper considers an innovative model of autonomous charging stations where a program implementing a scheduling algorithm and a set of jobs being scheduled are driven by the same common power source. It is assumed that one of the well-known local search metaheuristics—an evolutionary algorithm—is used for the scheduling process. The algorithm is designed to search for a sequence of charging jobs resulting in a schedule of the minimum length. Since processors with variable processing speeds can be used for computations, this has interesting consequences both from a theoretical and practical point of view. It is shown in the paper that the problem of choosing the right processor speed under given constraints and an assumed scheduling criterion is a non-trivial one. We formulate a general problem of determining the computation speed of the evolutionary algorithm based on the proposed model of a computational task and the adopted problem of scheduling charging jobs. The novelty of the paper consists of two aspects: (i) proposing the new model of the autonomous charging station operating according to the basics of edge computing; and (ii) developing the methodology for dynamically changing the computational speed, taking into account power and energy constraints as well as the results of computations obtained in the current iteration of the algorithm. Some approaches for selecting the appropriate speed of computations are proposed and discussed. Conclusions and possible directions for future research are also given.
... In this case, Vessel 3 sailed the same distance over the entire duration of its voyage, and the extra time gained, 16 hours, was factored into its schedule. This adjustment allowed Vessel 3 to reduce its speed, as expressed in Equation (12): (12) where V 3(reduced) is the reduced vessel speed of Vessel 3. schedule led to a 4-hour operation stop at Berth 1 between the 44th and 48th hour. Conversely, with the DT, the application reshuffled schedules, resulting in a mere 4-hour delay. ...
The maritime industry is a major carbon emission contributor. Therefore, the global maritime industry puts every effort into reducing carbon emissions in the shipping chain, which includes vessel fleets, ports, terminals, and hinterland transportation. A representative example is the carbon emission reduction standard mandated by the International Maritime Organization for international sailing ships to reduce carbon emissions this year. Among the decarbonization tools, the most immediate solution for reducing carbon emissions is to reduce vessel waiting time near ports and increase operational efficiency. The operation efficiency improvement in maritime stakeholders’ port operations can be achieved using data. This data collection and operational efficiency improvement can be realized using a digital twin. This study develops a digital twin that measures and reduces carbon emissions using the collaborative operation of maritime stakeholders. In this study, the authors propose a data structure and backbone scheduling algorithm for a port digital twin. The interactive scheduling between a port and its vessels is investigated using the digital twin. The digital twin’s interactive scheduling for the proposed model improved predictions of vessel arrival time and voyage carbon emissions. The result of the proposed digital twin model is compared to an actual operation case from the Busan New Port in September 2022, which shows that the proposed model saves over 75 % of the carbon emissions compared with the case.
... To facilitate the process of integrating real-time information, this implementation utilized a simulation package connected to an IoT platform. In a study published in [112], a framework was proposed for using DT to automate the scheduling of storage yards in ports. This system utilizes the IoT, virtual reality, and digital thread technology to create DT of the storage yard that can be used to accurately plan and coordinate the dispatch of cargo. ...
... is to establish a dynamic and imaginative workplace where representatives of production and robot companies collaborate closely together with students and researchers to create cutting-edge and efficient manufacturing techniques [24]. ...
Adoption of digital twin (DT) in smart factories, which simulates an actual system that is manufacturing conditions and updates them in real-time, increased the output and decreased the costs and energy use which were some ways that this manifested. Fast-changing consumer demands have caused a sharp increase in factory transition in addition to producing fewer life cycles of a product. Such scenarios cannot be handled by conventional simulation and modeling techniques; we suggest a general framework for automating the creation of simulation models that are data-driven as the foundation for smart factory DTs. Our proposed framework stands out thanks to its data-driven methodology, which takes advantage of recent advances in machine learning and techniques for process mining, constant model validation, and updating. The framework's objective is to completely define and reduce the requirement for specialist knowledge to get the appropriate simulation models. A case study is used to demonstrate our framework.
... A growing number of DT studies illustrate the potential of DT implementation to increase the effectiveness of certain port operations. These studies cover a wide range of different terminal operations and problems, including DT applications in automated storage yard scheduling [124], operating status monitoring for port cranes [111], and assisting truck dispatchers [113]. For example, the experimental results of a machine learning based DT implementation of the largest container terminal in Shanghai by Li et al. in paper [114] illustrate the potential of DT-based terminal operation. ...
Ports are striving for innovative technological solutions to cope with the ever-increasing growth of transport, while at the same time improving their environmental footprint. An emerging technology that has the potential to substantially increase the efficiency of the multifaceted and interconnected port processes is the digital twin. Although digital twins have been successfully integrated in many industries, there is still a lack of cross-domain understanding of what constitutes a digital twin. Furthermore, the implementation of the digital twin in complex systems such as the port is still in its infancy. This paper attempts to fill this research gap by conducting an extensive cross-domain literature review of what constitutes a digital twin, keeping in mind the extent to which the respective findings can be applied to the port. It turns out that the digital twin of the port is most comparable to complex systems such as smart cities and supply chains, both in terms of its functional relevance as well as in terms of its requirements and characteristics. The conducted literature review, considering the different port processes and port characteristics, results in the identification of three core requirements of a digital port twin, which are described in detail. These include situational awareness, comprehensive data analytics capabilities for intelligent decision making, and the provision of an interface to promote multi-stakeholder governance and collaboration. Finally, specific operational scenarios are proposed on how the port’s digital twin can contribute to energy savings by improving the use of port resources, facilities and operations.
... In the work of [32], digital twin technology is used to establish a virtual but realistic repository and organise the work inside it. Disturbances representing maintenance activities and equipment failures were introduced into typical scenarios. ...
The paper concerns the method of determining the probability of unproductive manipulations during operations, maintenance or repairs on an inland intermodal terminal. The method is mathematically based on the semi-Markov process. The developed method enables revision of unproductive manipulation frequency and duration. It provides an opportunity to analyse and change inland terminal operations so as to increase productivity.
... A DT framework can curate multiple algorithms and subject problems to a set of optimization tools. Numerous ML techniques have been implemented in the "algorithm center" by [42], where the appropriate algorithm is selected to match the problem. ...
In the era of industry 5.0, digital twins (DTs) play an increasingly pivotal role in contemporary society. Despite the literature’s lack of a consistent definition, DTs have been applied to numerous areas as virtual replicas of physical objects, machines, or systems, particularly in manufacturing, production, and operations. One of the major advantages of digital twins is their ability to supervise the system’s evolution and run simulations, making them connected and capable of supporting decision-making. Additionally, they are highly compatible with artificial intelligence (AI) as they can be mapped to all data types and intelligence associated with the physical system. Given their potential benefits, it is surprising that the utilization of DTs for warehouse management has been relatively neglected over the years, despite its importance in ensuring supply chain and production uptime. Effective warehouse management is crucial for ensuring supply chain and production continuity in both manufacturing and retail operations. It also involves uncertain material handling operations, making it challenging to control the activity. This paper aims to evaluate the synergies between AI and digital twins as state-of-the-art technologies and examines warehouse digital twins’ (WDT) use cases to assess the maturity of AI applications within WDT, including techniques, objectives, and challenges. We also identify inconsistencies and research gaps, which pave the way for future development and innovation. Ultimately, this research work’s findings can contribute to improving warehouse management, supply chain optimization, and operational efficiency in various industries.
... Gao vd. [52] belirsiz liman sevkiyatı için dijital ikiz ile etkinleştirilmiş otomatik bir depolama sahası çizelgeleme çerçevesi önermişlerdir. Otomatikleştirilmiş depolama alanı çizelgelemesindeki dijital ikiz uygulamaların operatörlerin optimizasyon kararları almasına yardımcı olduğunu göstermiştir. ...
In this study, it is aimed to examine the processes from end to end within the scope of the data digitization project in the production facility of a leading company operating in the garment industry and to make data-oriented process designs using new generation information technologies. Accordingly, it is targeted to create the necessary process infrastructures for making digital twin models, which is a newly developing and rapidly growing technology. In the study, first of all, process maps were created and the constantly changing data of the processes were obtained with the help of sensors and interfaces and transferred to the system. Then, by establishing a connection between the process-based times taken from the machines on the production line and the characteristics of the product to be produced, how long it will take for any product to be completed when it enters the process was estimated on the Knime platform using linear regression, polynomial regression, gradient boosting decision forest regression and random forest regression algorithms. According to the estimation results, it was determined that the random forest regression model had the highest R2 and lowest error metric values, and this regression model was integrated into the ERP infrastructure. In addition, a production scheduling study was designed according to the estimated production times and various parameters on the line. The study is important in terms of establishing the infrastructure of a intelligent system that can decide on its own, and it is anticipated that it will contribute to the creation of the digital twins of the processes.
... Using this mode, Zhou et al. developed a DSS with DT-based resilience analysis as an efcient 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 fnding for AGVs [28]. ...
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.
... A growing number of DT studies illustrate the potential of DT implementation to increase the effectiveness of certain port operations. These studies cover a wide range of different terminal operations and problems, including DT applications in automated storage yard scheduling [101], operating status monitoring for port cranes [13], and assisting truck dispatchers [16]. For example, the experimental results of a machine learning based DT implementation of the largest container terminal in Shanghai by Li et al. in paper [17] illustrate the potential of DT-based terminal operation. ...
Ports are striving for innovative technological solutions to cope with the ever-increasing growth of transport, while at the same time improving their environmental footprint. An emerging technology that has the potential to substantially increase the efficiency of the multifaceted and interconnected port processes is the digital twin. Although digital twins have been successfully integrated in many industries, there is still a lack of cross-domain understanding of what constitutes a digital twin. Furthermore, the implementation of the digital twin in complex systems such as the port is still in its infancy. This paper attempts to fill this research gap by conducting an extensive cross-domain literature review of what constitutes a digital twin, keeping in mind the extent to which the respective findings can be applied to the port. It turns out that the digital twin of the port is most comparable to complex systems such as smart cities and supply chains, both in terms of its functional relevance as well as in terms of its requirements and characteristics. The conducted literature review, considering the different port processes and port characteristics, results in the identification of three core requirements of a digital port twin, which are described in detail. These include situational awareness, comprehensive data analytics capabilities for intelligent decision making, and the provision of an interface to promote multi-stakeholder governance and collaboration. Finally, specific operational scenarios are proposed on how the port's digital twin can contribute to energy savings by improving the use of port resources, facilities and operations.
... The platform completed the real-time data driving and displayed it in Unity 3D. It realized the rapid modeling of the virtual twin scene and the visual display driven by the realtime data of the physical world [102][103][104]. ...
This work aims to explore the impact of Digital Twins Technology on industrial manufacturing in the context of Industry 5.0. A computer is used to search the Web of Science database to summarize the Digital Twins in Industry 5.0. First, the background and system architecture of Industry 5.0 are introduced. Then, the potential applications and key modeling technologies in Industry 5.0 are discussd. It is found that equipment is the infrastructure of industrial scenarios, and the embedded intelligent upgrade for equipment is a Digital Twins primary condition. At the same time, Digital Twins can provide automated real-time process analysis between connected machines and data sources, speeding up error detection and correction. In addition, Digital Twins can bring obvious efficiency improvements and cost reductions to industrial manufacturing. Digital Twins reflects its potential application value and subsequent potential value in Industry 5.0 through the prospect. It is hoped that this relatively systematic overview can provide technical reference for the intelligent development of industrial manufacturing and the improvement of the efficiency of the entire business process in the Industrial X.0 era.
... Gao vd. [52] belirsiz liman sevkiyatı için dijital ikiz ile etkinleştirilmiş otomatik bir depolama sahası çizelgeleme çerçevesi önermişlerdir. Otomatikleştirilmiş depolama alanı çizelgelemesindeki dijital ikiz uygulamaların operatörlerin optimizasyon kararları almasına yardımcı olduğunu göstermiştir. ...
Bu çalışmada, hazır giyim sektöründe faaliyet gösteren öncü bir firmanın üretim tesisinde, veri dijitalleştirme projesi kapsamında süreçlerin uçtan uca incelenmesi ve yeni nesil bilgi teknolojileri kullanılarak veri odaklı süreç tasarımlarının yapılması amaçlanmıştır. Buna bağlı olarak, yeni gelişen ve hızlı büyüyen bir teknoloji olan dijital ikiz modellerinin yapılabilmesi için gerekli olan süreç altyapılarının oluşturulması hedeflenmiştir. Yapılan çalışmada ilk olarak, süreç haritaları oluşturulmuş ve süreçlere ait sürekli değişen verilerin sensörler ve arayüzler yardımıyla elde edilerek sisteme aktarılması sağlanmıştır. Daha sonra, üretim hattındaki makinelerden alınan süreç bazlı süreler ile üretilecek ürüne ait nitelikler arasında bağlantı kurularak, herhangi bir ürünün sürece girdiğinde ne kadar sürede tamamlanacağı lineer regresyon, polinomal regresyon, gradyan destekli karar ormanı regresyonu ve rassal orman regresyon algoritmaları kullanılarak Knime platformunda tahmin edilmiştir. Yapılan tahmin sonuçlarına göre rassal orman regresyon modelinin, en yüksek R2 ve en düşük hata metrik değerlerine sahip olduğu tespit edilmiş ve bu regresyon modeli ERP altyapısına entegre edilmiştir. Ayrıca, tahmin edilen üretim süreleri ve hat üzerindeki çeşitli parametrelere göre üretim çizelgeleme çalışması tasarımı yapılmıştır. Yapılan çalışma, kendi kendine karar verebilen akıllı bir sistemin altyapısının oluşturulması bakımından önemli olup süreçlerin dijital ikizlerinin oluşturulmasında katkı sağlayacağı öngörülmektedir.
... PoR aspires to create a DT of the port and facilitate autonomous shipping in the future [13] Control A framework of intelligent production management and control methods based on DT, promoting the development from automation and digitization to networking and collaboration [14] A system framework of a discrete matrix-based smart port management model. The main limitation is to build a consistent model for DT throughout the port process, which includes topics of common understanding of interfaces and standardization and efficient data flow [15] Logistics A framework for data-driven digital twin generation and reinforcement learning to address challenges encountered in real-time scenes rather than historical data [16] Research focuses on the validity of the framework and the implications of capturing uncertainty for port optimization, which determines optimal vehicle scheduling policies based on simulated performance predictions [17] System Variability for system modeling so that experimental DT of subcomponents can be easily added, removed, or swapped [18] A DT-based resiliency analysis decision support system (DSS) assesses the port's resilience under potentially disruptive events [19] of materials. This paper reconstructs the port system based on the digital twin concept on the basis of original cyberphysics. ...
The increase in port scale and business complexity has led to an increased demand for comprehensive and lean control on ports. The current operation mode is facing the bottleneck of the increasingly significant production efficiency and performance. Digital twin (DT) technology realizes holographic visual management and control patterns using cyber-physical fusion and promotes the transformation of a port to an intelligent operation mode. In this paper, the framework of a digital twin application system is proposed based on the analysis of business characteristics of large-scale comprehensive ports. Construction methods and technologies such as digital twin modeling, global ubiquitous perception, data mapping, and model fusion are analyzed. With regard to the construction needs of Qingdao Port’s digital twin system, this paper presents a case study and illustrates the overall design process and function of the digital twin system for typical terminals. The system realizes the intelligent operation of the port with the core functions of three-dimensional visual monitoring and optimal dispatching based on real-time perception data. This paper serves as a feasible reference for future intelligent development of large ports and the application of digital twin technology.
... For example, Yu et al. [99] developed a DT model using big data techniques to predict pavement performance. Gao et al. [100] also proposed a DT framework for the automation of scheduling of storage yards in ports. Other connected keywords, such as ''service life prediction'', ''shm'' (structural health monitoring), ''asset management'', ''decision making'', ''damage detection'', and ''anomaly detection'', are all related to the IDT applications in the O&M phase. ...
Digital Twins (DTs) have received considerable attention as an emerging technology in recent years, which can offer advantages for improving the performance of infrastructures. However, the inherent complexity of infrastructures alongside the nascent nature of digital twins in the Architecture, Engineering, and Construction (AEC) industry hinders the adoption of infrastructure digital twins (IDTs). The lack of common understanding among different stakeholders has been noted as one of the most significant roadblocks to implementing IDTs in practice. This study is a quantitative attempt to address this gap by providing different stakeholders with a multi-layer knowledge map by analyzing 139 identified IDTs in three levels of bibliometric and social network analyses. First, knowledge themes are extracted from the most-frequent journals to provide an overview of IDT knowledge. Second, a combination of co-citation analysis and social network theories illustrated six clusters of IDT knowledge and their relationships. Third, the co-occurrence network of keywords revealed where, why, and what enabling technologies have been employed so far. These findings were synthesized into a three-layer knowledge map to not only illustrate the maturity level and evolution potentials of each layer but also serve as a hierarchical strategic plan recommending future direction to decision-makers, researchers, and practitioners.
... DT could curate multiple algorithms in the same platform and subject problems to this set of optimization tools. Gao et al. (2022) implemented multiple ML techniques, in the "algorithm center". The appropriate algorithm is selected to match the problem from the Algorithm Center. ...
In the era of Industry 4.0, digital twins are at a pivotal phase. For a concept that is so inconsistently defined in the literature, it has been used for many applications, especially in manufacturing, production, and operations. DT not only allows for supervision and running simulations, but it also supports AI applications since it is mapped to all types of data and Intel on the physical object. On the other hand, warehouses have been subject to little digitization over the years. Warehouse management is at the very core of both manufacturing and retail operations, ensuring supply chain and production continuity. It is also a conjunction of uncertain material handling activities. It could easily benefit from the Information visibility and the smart features supplied by digital twins and machine learning. In this perspective, this paper examines the use cases of warehouse digital twins (WDT). This study aims to assess the maturity of AI application within WDT, namely techniques, objectives, and challenges. Consequently, inconsistencies are identified and research gaps are presented, making way for future development and innovation.
... The optimal paths with the minimum transporting time are planned for AGVs by Q-learning. The developed automated container terminal digital twin system helps to observe and collect the real-time operation data from the physical space as reported in Gao et al. [22]. Then, the information of transportation tasks and AGV operation is input to Qlearning for deciding the destinations and paths. ...
The complex and dynamic environment in an automated container terminal (ACT) increases the difficulty of path planning, especially for automated guided vehicles (AGVs). Digital twin is an essential means of characterizing complex production systems as the physical objects can be synchronized in the virtual space. Machine learning is also a popular way to solve path planning problems. This study combines digital twin and machine learning to tackle AGV path planning problems in the time-changing operation environment. A digital twin-based AGV scheduling approach is developed to obtain the real-time data from the physical ACT. Based on the information of the dynamic factors obtained, a mathematical model is formulated to minimize the transportation time of AGVs. Subsequently, the path planning schemes solved by machine learning are used asinput to the virtual ACT for validation and optimization. The optimized solution is further compared to a common path plan algorithm without applying digital twin. The experimental results show the superiority of the proposed method, which can provide better decision support for ACT operation.
... They also detect the load on key components and checked wear. Gao et al. [10] propose a digital dual-function automated yard scheduling framework for uncertain port scheduling, and use the digital twin technology to establish virtual reality storage fields and their connections, thus improving the flexibility of scheduling. ...
The numerical control machine tool is an important part of manufacturing. The speed and accuracy of the machine tool simulation and the monitoring of machine physical model movement, model collision, and operation health are the most common bottlenecks. The previous studies on these problems are discrete and poor in real time. In this paper, combined with the digital twin (DT) technology, a perception-monitor-feedback system architecture is constructed. Based on the improved Gilbert–Johnson–Keerthi (GJK) distance algorithm, the collision information of tool and machine tool can be better detected during the simulation and monitoring of machine tool movement, and the more real workpiece shape can be displayed. Based on the model synchronous motion driven by the production perception data and its potential information extraction, the tool wear online monitoring is carried out. The tool wear online monitoring is performed based on the production perception data–driven model synchronous movement and extraction of its potential information. Finally, the usability and efficiency of the system are verified by an example of a typical shaft part processed by a numerical control machine tool.
... These technologies have been implemented in various applications such as virtualizing manufacturing systems, processes simulation and optimization, and manufacturing operation management (Nguyen et al., 2022). For instance, the integration of VR and DT in automated storage yard scheduling can help operators make optimization decisions (Gao et al., 2022). On the other hand, one of the core predictive technologies from DT is Discrete Event Simulation (DES), enabling the systems to be optimized before evaluation as well as the opportunity for self-optimization (Reed et al., 2021;de Paula Ferreira et al., 2020). ...
Virtual reality (VR) and discrete event simulation (DES) have become fundamental technologies of industry 4.0. There is a growing interest in such technologies in both academic and industry sectors. Nevertheless, few studies have reported the use of them simultaneously. This study develops a DES model and proposes an integrated approach combining self-reported and objective measurements to determine the VR effectiveness, based on data collection and analysis of 72 volunteers randomly assigned either a VR system or a laptop computer. The model represents a real manufacturing system where the user can move around, interact with objects, modify variables, run different scenarios, and identify the best one based on the system’s key performance indicators. Results showed that experimentation time is significantly less when users interact with the VR interface, and there is no significant difference between the two interfaces when comparing the decision-making time. However, the VR users obtained a higher percentage of correct answers. Finally, self-reported feedback indicated users preferred the VR system, in addition to the fact that discomfort and presence questionnaires exhibit typical values of the VR experience, and the usability questionnaire yields higher values than the laptop computer.
Increasing freight volumes and challenging environments in seaports and container terminals worldwide require streamlined and reliable operations. Digital twins are seen as important drivers of the digitalization in seaports by providing a basis for higher transparency, control and data-driven decision making. In this context, however, the concept is rarely studied, and implementation issues are not comprehensively discussed. The paper presents an exploratory study of digital twins in seaports based on a literature review and case studies. The analysis reveals a standardization deficit for digital twin implementations, an inflationary and improper use of the term digital twin, and fields of research that need to be explored further. The application of optimization methods and the integration of simulation-based optimization in the field of seaports and container terminals is examined, due to its relevance for digital twins. Important lessons learned can be taken from the most advanced implementations, integrating simulations and emulations with optimization methods. An in-depth examination of multiple case studies and discussions with global port leaders yields valuable perspectives on the varied levels of digital twin implementations being applied today, including insights into the most advanced implementations currently being used in ports and container terminals. As a result of the analyses conducted, various research directions and a research agenda are presented.
The urban domestic waste produced by population centers has generated a growing environmental problem in recent years. As a result, the waste management industry has significantly developed in the country. However, since these processes are highly manual, their efficiency is not enough to meet the demand.The previously described problems can be solved with an improvement in processes, and since Industry 4.0 and especially Digital Twins can be in charge of improving processes, it is necessary to develop a comprehensive methodology for generating these solutions that faces current and future challenges in the industry, especially in the waste management industry. This document proposes a definition of digital twins, structures the methodological development that includes an architecture and a layered framework, and a series of methodological steps for the application of the digital twin according to the level of scope defined by the end-user. It can be highlighted how digital twins allow greater efficiency and precision in waste management since they provide a virtual real-time representation of the waste management system and allow simulations and testing to optimize processes. Digital twins also enable more informed decision-making in waste management, as they provide a detailed visualization of the entire system, which facilitates the identification of problems and opportunities for improvement.KeywordsDigital TwinsApplication methodologyImprovement of Production Systems and Services
Digital transformation has emerged as a crucialaspect in the mining industry to enhance productivity,efficiency, and availability of various machines and equipment.The integration of digital twin technology in the mining industryhas demonstrated significant potential in addressing challengespertaining to maintenance, production, and safety. This paperpresents a comparative study of developed digital twins invarious industrial applications, including building informationmodeling, energy management, manufacturing, healthcare, andoptimization. The aim of this research is to design a digital twinof a Stacker Machine (SM) in the experimental open pit mine ofOCP Benguerir, organized into four layers: physical and linklayer, application layer, and control layer. The implementationstudy conducted on the SM used in mining demonstratespractical results through the simulation of the system'sautomatism. The resulting digital twin can be employed tosimulate critical scenarios and monitor the behavior of the SMto enhance safety, reliability, and availability.
The integrated scheduling of quay cranes, yard cranes, and internal vehicles in a port allows for further optimization of resource allocation on a global scale. Since the scheduling objects are all mobile equipment and their mutual waiting time needs to be reduced as much as possible, new requirements are brought for integrated scheduling. Uncertain operation time is a common phenomenon in ports. How to
ensure the effectiveness and robustness of integrated schedules under ncertainties is worth studying. Moreover, although the cascading effects of operation time fluctuation is an important factor affecting the robustness of schedules, less attention has been paid to it. To solve this problem, firstly, a mixed integer nonlinear programming model for integrated scheduling in a port that allows internal vehicles to travel deep into the yard is formulated. A genetic algorithm for solving the model is designed. Then, “gaps” between adjacent operations in schedules are focused on, and the complex network structural entropy theory is used to construct an anti-cascade effect and robustness evaluation index of schedules. In addition, a scheduling plan generation framework that gives a way to use the evaluation index is proposed. Specifically, the anticascade effect and robustness evaluation indices of scheduling plans whose objective function values are within a given threshold are calculated, and the final scheduling plan with the largest index is selected. Finally,
the feasibility and effectiveness of the proposed method are verified by extensive experiments.
In the era of digitalization, many technologies are evolving, namely, the Internet of Things (IoT), big data, cloud computing, artificial intelligence (IA), and digital twin (DT) which has gained significant traction in a variety of sectors, including the mining industry. The use of DT in the mining industry is driven by its potential to improve efficiency, productivity, and sustainability by monitoring performance, simulating results, and predicting errors and yield. Additionally, the increasing demand for individualized products highlights the need for effective management of the entire product lifecycle, from design to development, modeling, simulating, prototyping, maintenance and troubleshooting, commissioning, targeting the market, use, and end-of-life. However, the problem to be overcome is how to successfully integrate DT into the mining business. This paper intends to shed light on the state of art of DT case studies focusing on concept, design, and development. The DT reference architecture model in Industry 4.0 and value-lifecycle-management-enabled DT are also discussed, and a proposition of a DT multi-layered architecture framework for the mining industry is explained to inspire future case studies.
The sustainable development of port operation management is strongly related to the energy consumption of production at automated container terminals (ACTs). This paper focuses on the production activities at a container yard, which is the primary facility of ACTs. A digital twin-based approach is proposed to optimize the operation of an automatic stacking crane (ASC) handling containers in terms of energy consumption. A virtual container yard that syncs with a physical container yard in the ACT digital twin system for observation and validation is developed. A mathematical model is established to minimize the total energy consumption of completing all tasks. Then, the Q-learning algorithm is adapted to optimize a solution based on the operating data from the ACT digital twin system. Numerical experiments are conducted to demonstrate the effectiveness of the proposed approach by comparing it with two other solution algorithms, viz., genetic algorithm (GA) and particle swarm optimization (PSO). The total energy consumption of two operation strategies (i.e., centralized and decentralized) are also compared using the proposed digital twin-based approach. With digital twin, the operational environment and energy consumption are visualized to support optimization and management of ASCs. Managers and operators can choose an appropriate strategy according to the designated sustainable goal.
This book of proceedings contains peer-reviewed papers that were presented at the 29th ISTE International Conference on Transdisciplinary Engineering (TE2022), organized by System Design and Management (SDM) at the Massachusetts Institute of Technology in Cambridge, MA, United States from July 5–8, 2022.
TE2022 brought together a diverse global community of scholars and practitioners in dialogue and reflection on engineering itself. Engineering is changing rapidly. The
connectedness of the world’s most critical systems along with rapid advancement of
methods push us to ask “How will we teach, research, and practice engineering?”
The state of the direction of crushing materials in the mining industry is considered. The method of static calculation for the strength of the frame of the cone crusher KSD – 900 in two positions is given. The application of the machine at various stages of crushing is demonstrated. The comparison with other crushers of the mining industry is carried out. The design and operating principle of the digital twin of the cone crusher of medium crushing are described. The advantages and disadvantages of the machine are given. The finite element method of the CAD/CAM/CAE system NX and the module “Advanced Simulation” are considered. Today’s significance of engineering analysis is described. The calculation schemes of the cone crusher stand in several static positions are given: at the moment of lifting the stationary cone and lid, when non-crushing material enters and when the stationary cone and lid of the cone crusher are lowered. The idealization of the frame in the CAD/CAM/CAE NX system and the “Advanced Simulation” module of the cone crusher are shown to reduce the load on the calculating machine. It leads to a reduction in the calculation time of the model. Tables of external loads under the own weight of the working units of the machine are presented. The calculation of comparing the maximum design stresses with those allowed for the selected material and determining the safety factor of stand is shown.
Digitalization in mining industry has become animportant component nowadays in order to improve theproductivity, the efficiency while increasing the availability ofmachines and different equipment The use of digital twin as afeature in mining industry has shown promising results to dealwith its multiple challenges such as maintenance, production,energy consumption. In this paper, a comparative study ispresented of developed digital twins in different applicationssuch as energy management, manufacturing, industrial andcampus, the goal is to design a conceptual architecture of aproposed digital twin within presenting a case study of a stackermachine in the experimental open pit mine of Benguerir. Thisdesign presented by 4 layers : physical, Data pre-processing,Edge computing, and cloud data, the goal is to work oninteractions between these layers to enhance the performance ofdifferent mining machines. The case study elucidates a practicalresult on Stacker machine used in mining industry simulatingthe mechanism and the automatism of this system. Thisdeveloped digital twin will be used to simulate critical scenariosand see the behavior of the stacker components in order tooptimize the usage of this machine increasing its availability andreliability.
The paper presents an Integrated Maintenance Decision Making Model (IMDMM) concept for cranes under operation especially into the container type terminals. The target is to improve cranes operational efficiency through minimizing the risk of the Gantry Cranes Inefficiency (GCI) results based on the implementation of the Digital Twins concept for maintenance purposes. The proposed model makes a joint transportation process and crane maintenance scheduling, relevant to assure more robust performances in stochastic environments, as well as to assess and optimize performances at different levels, from components and transport device to production systems (container terminal). The crane operation risk is estimated with a sequentialMarkov chainMonte Carlo simulation model and the optimization model behind of IMDMM is supported through the Particle Swarm Optimization algorithms because the objective function a non-linear stochastics problem with bounded constrains. The developed model allows the container terminal operators (management process) to obtain a maintenance schedule that minimizes the GCI (holistic indicator), as well as establishing the desired level of risk. The paper demonstrates the effectiveness of the proposed maintenance decision making concept model for cranes under operation using data from of a real container terminal (case study).
Realizing the digital thread is essential for linking and orchestrating data across the product lifecycle in smart manufacturing. Linking heterogeneous lifecycle data is critical to maintain associativity and traceability in a digital thread. Recently, researchers have successfully leveraged ontology models with knowledge graphs in engineering domains for threading different lifecycle data. One of the most successful of such efforts is OntoSTEP which enables the formal capture of information embedded in the STandard for Exchange of Product model data (STEP) data representation, or ISO 10303. Meanwhile, an emerging inspection standard, called the Quality Information Framework (QIF), has garnered significant attention as it can bring quality information into the digital thread. Implementing more automated methods for product quality assurance is challenging due to the lack of unified information models from design to inspection. To this end, we propose an approach to fuse as-designed data represented in STEP and as-inspected data represented in QIF in a standards-based digital thread based on ontology with knowledge graphs. Specifically, we present an automated pipeline for generating knowledge graphs representing STEP and QIF data, a mapping implementation to integrate STEP and QIF knowledge graphs, and rules and queries to demonstrate the integration’s potential for better decision making with respect to product quality assurance.
Digital twin represents a fusion of the informational and physical domains, to bridge the material and virtual worlds. Existing methods of digital twin modeling are mainly based on modular representation, which limits guidance of the modeling process. Such methods do not consider the components or operational rules of the digital twin in detail, thereby preventing designers from applying these methods in their fields. With the increasing application of digital twin to various engineering fields, an effective method of modeling a multi-dimensional digital twin at the conceptual level is required. To such an end, this paper presents a method for the conceptual modeling of a digital twin based on a five-dimensional digital twin framework to represent the complex relationship between digital twin objects and their attributes. The proposed method was used to model the digital twin of an intelligent vehicle at the concept level.
Various kinds of engineering software and digitalized equipment are widely applied through the lifecycle of industrial products. As a result, massive data of different types are being produced. However, these data are hysteretic and isolated from each other, leading to low efficiency and low utilization of these valuable data. Simulation based on theoretical and static model has been a conventional and powerful tool for the verification, validation, and optimization of a system in its early planning stage, but no attention is paid to the simulation application during system run-time. With the development of new-generation information and digitalization technologies, more data can be collected, and it is time to find a way for the deep application of all these data. As a result, the concept of digital twin has aroused much concern and is developing rapidly. Dispute and discussions around concepts, paradigms, frameworks, applications, and technologies of digital twin are on the rise both in academic and industrial communities. After a complete search of several databases and careful selection according to the proposed criteria, 240 academic publications about digital twin are identified and classified. This paper conducts a comprehensive and in-depth review of these literatures to analyze digital twin from the perspective of concepts, technologies, and industrial applications. Research status, evolution of the concept, key enabling technologies of three aspects, and fifteen kinds of industrial applications in respective lifecycle phase are demonstrated in detail. Based on this, observations and future work recommendations for digital twin research are presented in the form of different lifecycle phases.
The efficiency of automated container terminals primarily depends on the synchronization of automated-guided vehicles (AGVs) and automated cranes. Accordingly, we study the integrated rail-mounted yard crane and AGV scheduling problem as a multi-robot coordination and scheduling problem in this paper. Based on a discretized virtualized network, we propose a multicommodity network flow model with two sets of flow balance constraints for cranes and AGVs. In addition, two side constraints are introduced to deal with inter-robot constraints to reflect the complex interactions among terminal agents accurately. The Alternating Direction Method of Multipliers (ADMM) method is adopted in this study as a market-driven approach to dualize the hard side constraints; therefore, the original problem is decomposed into a set of crane-specific and vehicle-specific subtasks. The cost-effective solutions can be obtained by iteratively adjusting both the primal and dual costs of each subtask. We also compare the computational performance of the proposed solution framework with that of the resource-constrained project scheduling problem (RCPSP) model using commercial solvers. Comparison results indicate that our proposed approach could efficiently find solutions within 2% optimality gaps. Illustrative and real-world instances show that the proposed approach effectively serves the accurate coordination of AGVs and cranes in automated terminals.
Virtual models boost smart manufacturing by simulating decisions and optimization, from design to operations, explain Fei Tao and Qinglin Qi. Virtual models boost smart manufacturing by simulating decisions and optimization, from design to operations, explain Fei Tao and Qinglin Qi. An illustration of a digital twin city
Rubber-tyred gantry cranes are one of the major sources of carbon dioxide emissions in container terminals. In a move to green transportation, the traditional diesel powered cranes are being converted to electric ones. In this paper, we study the deployment of electric powered gantry cranes (ERTGs) in container terminal yards. Cranes always move in-between blocks to serve different workload. ERTGs use electricity for most movements but switch to diesel engines to allow inter-block transfers between unaligned blocks. We exploit this feature and propose to consider simultaneously the CO2 emissions and workload delays to develop carbon-efficient deployment strategies. Moreover, unlike previous works we consider the workload uncertainty, and model the problem as a two-stage stochastic program. A sample average approximation framework with Benders decomposition is employed to solve the problem. Multiple acceleration techniques are proposed, including a tailored regularised decomposition approach and valid inequalities. A case study with sample data from a major port in East China show that our proposal could reduce significantly CO2 emissions with only a marginal compromise in workload delays. Our numerical experiments also highlight the significance of the stochastic model and the efficiency of the Benders algorithms.
The digital thread links disparate systems across the product lifecycle to support data curation and information cultivation and enable data-driven applications, e.g., digital twin. Realizing the digital thread requires the integration of semantically-rich, open standards to facilitate the dynamic creation of context based on multiple viewpoints. This research develops such an approach to link as-planned (ISO 6983) to as-fabricated (MTConnect) product data using dynamic time warping. Applying this approach to a production part enabled the designer to make a more optimal decision from the perspective of the product lifecycle that would have otherwise been challenging to identify.
Nowadays, along with the application of new-generation information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively.
Optimizing collaborative operations for yard cranes (YCs) and yard trucks (YTs) is vital to the overall performance of a container terminal. This research investigates four different hybrid approaches developed for dealing with yard crane scheduling problem (YCSP) and yard truck scheduling problem (YTSP) simultaneously for export containers in the yard side area of a container terminal. First, these approaches use a load-balancing heuristic to assign containers to YCs evenly. Following this, each of them employs a specific heuristic/metaheuristic, such as genetic algorithm (GA), particle swarm optimization (PSO) or subgroups PSO (SGPSO), to generate alternative container loading sequences for each YC. Finally, a simulation model is used to simulate loading and transporting of these export containers, evaluate alternative planning results, and finally output the best planning result. Experiments have been conducted to compare these hybrid approaches. The results show Hybrid4 (SGPSO) outperforms Hybrid1 (Sort-by-bay), Hybrid2 (GA), and Hybrid3 (PSO) in terms of makespan.
In this paper, a Decision Support System (DSS) with digital twinning-based resilience analysis is proposed as a modern tool for port resilience computation and updating. The proposed DSS assesses the resilience of a port under possible disruptive events given its design, operations and potential pre-defined post-event recovery actions to mitigate the impact of the disruption. Digital twinning provides the fidelity required to realistically predict port performance with taken post-event recovery actions under various possible disruptive events. In addition to hedging against impacts from probabilistically known disruption events, this approach also enables inclusion of ordinary operational uncertainties within the resilience evaluation. This is not generally feasible with other existing resilience quantification approaches. To tackle computational challenges of applying a digital twin for real-world size applications, an optimal computing budget allocation policy is adopted to improve computational efficiency. Results of numerical experiments using a real-world size port demonstrate the effectiveness of the proposed DSS and criticality of accounting for ordinary uncertainties in operations in resilience estimation.
To fulfill increasingly difficult and demanding tasks in the ever-changing complex world, intelligent industrial products are to be developed with higher flexibility and adaptability. Digital twin (DT) brings about a possible means, due to its ability to provide candidate behavior adjustments based on received “feedbacks” from its physical part. However, such candidate adjustments are deterministic, and thus lack of flexibility and adaptability. To address such problem, in this paper an extended concept – evolutionary digital twin (EDT) and an EDT-based new mode for intelligent industrial product development has been proposed. With our proposed EDT, a more precise approximated model of the physical world could be established through supervised learning, based on which the collaborative exploration for optimal policies via parallel simulation in multiple cyberspaces could be performed through reinforcement learning. Hence, more flexibility and adaptability could be brought to industrial products through machine learning (such as supervised learning and reinforcement learning) based self-evolution. As a primary verification of the effectiveness of our proposed approach, a case study has been carried out. The experimental results have well confirmed the effectiveness of our EDT based development mode.
Digital twin takes Industrial Internet as a carrier deeply coordinating and integrating virtual spaces with physical spaces, which effectively promotes smart factory development. Digital twin-based big data learning and analysis (BDLA) deepens virtual and real fusion, interaction and closed-loop iterative optimization in smart factories. This paper proposes a digital twin-based big data virtual and real fusion (DT-BDVRL) reference framework supported by Industrial Internet towards smart manufacturing. The reference framework is synthetically designed from three perspectives. The first one is an overall framework of DT-BDVRL supported by Industrial Internet. The second one is the establishment method and flow of BDLA models based on digital twin. The final one is digital thread of DT-BDVRL in virtual and real fusion analysis, iteration and closed-loop feedback in product full life cycle processes. For different virtual scenes, iterative optimization and verification methods and processes of BDLA models in virtual spaces are established. Moreover, the BDLA results can drive digital twin running in virtual spaces. By this, the BDLA results can be validated iteratively multiple times in virtual spaces. At same time, the BDLA results that run in virtual spaces are synchronized and executed in physical spaces through Industrial Internet platforms, effectively improving the physical execution effect of BDLA models. Finally, the above contents were applied and verified in the actual production case study of power switchgear equipment.
Recent findings have shown that Digital Twin served multiple constituencies. However, the dilemma between the scope and scale needs a sophisticated reference architecture, a right set of technologies, and a suitable business model. Most studies in the Digital Twin field have only focused on manufacturing and proposed explicit frameworks and architecture, which faced challenges to support different integration levels through an agile process. Besides, no known empirical research has focused on exploring relationships between Digital Twin and mass individualization. Therefore, the principal objective of this study was to identify suitable Industry 4.0 technologies and a holistic reference architecture model to accomplish the most challenging Digital Twin enabled applications. In this study, a Digital Twin reference architecture was developed and applied in an industrial case. Also, Digital Twin as a Service (DTaaS) paradigm utilized for the digital transformation of unique wetlands with considerable advantages, including smart scheduled maintenance, real-time monitoring, remote controlling, and predicting functionalities. The findings indicate that there is a significant relationship between Digital Twin capabilities as a service and mass individualization.
To improve the operational efficiency of container terminals, it is important to consider the coordination of different types of container-handling equipment, which typically include vehicles, yard cranes and quay cranes. This paper addresses the integration of scheduling each constituent of handling equipment in an automated container terminal, in order to minimise the loading element of the ship’s berthing time. A mixed-integer programming (MIP) model was developed to mathematically formulate this challenge. Small-sized problems can be solved optimally using existing solver. In order to obtain approximately optimal solutions for large-sized problems, an adaptive heuristic algorithm was created that can adjust the parameters of a genetic algorithm (GA), according to the observed performance. Experiments were carried out for both small-sized and large-sized problems to analyse the impact of equipment used in the loading process on berthing and computation times, as well as to test the efficiency of our proposed adaptive GA in solving this integrated problem.
In recent years, Digital Twins (DT) have been implemented in different industrial sectors, in several applications areas such as design, production, manufacturing, and maintenance. In particular, maintenance is one of the most researched applications, as the impact of the execution of maintenance task may have a great impact in the business of the companies. For example, in sector such as energy or manufacturing, a maintenance activity can cause the shutdown of an entire production line, or in the case of a wind turbine inspection, may face the safety of an operator to measure a simple indicator. Hence, the application of more intelligent maintenance strategies can offer huge benefits. In this context, this paper focuses on the review of DT applications for maintenance, as no previous work has been found with this aim. For instance, both “Digital Twin” and “maintenance” concepts and strategies are described in detail, and then a literature review is carried out where these two concepts are involved. In addition to identifying and analyzing how DTs are currently being applied for maintenance, this paper also highlights future research lines and open issues.
The great potential of digital twin (DT) in supporting smart industrial systems has brought huge requirements for on-demand DT-based simulation, a particularly useful and sustainable means, to assist various decision-making. However, there are major challenges to efficiently build and update the DT-based simulation system and provide simulation as a service (SimaaS): 1) virtualization machine based heavyweight methods to create simulation environments for DT models consume too much resource and time; 2) DT-based simulation systems in the cloud or developers’ desktops could not well support the real-time response and synchronize with the physical counterparts at the edge of the network. Therefore, a methodology of container virtualization based simulation as a service (CVSimaaS) is put forward to utilize lightweight containers to realize convenient DT system deployment and less resource consumption with high efficiency. Then a device-edge-cloud system architecture with a formal process are proposed to support the CVSimaaS paradigm. A matrix based management and scheduling model for computing infrastructure, container images and services is established to support the efficient CVSimaaS process. Finally, the methodology is applied to building a DT-based simulation system for intelligent manufacturing. The results show that the DT-based simulation system can be 1) easily deployed to heterogeneous infrastructure and terminals at the cloud, edge and device, and 2) parallelly scheduled and operated on high performance cloud/edge on demand for large-scale online analysis.
Ports and terminals have evolved and from the 2010s have entered into a fifth stage of evolution characterized by their digital transformation and alignment with Industry 4.0 practices. Co-operation among agents is a key element and integration not only involves Port Authorities, Terminals and Port users and related Port Services Providers but the city, port's hinterland and well beyond the Global Supply Chain. Internet of Things and sensing solutions, cybersecurity, horizontal and vertical system integration, cloud computing, 3D printing and additive manufacturing, big data and business analytics, augmented reality and simulation and modeling are the pillars of Industry 4.0. Some of those are mature enough in the port and maritime industry. However, others remain in their earliest stages in this business and thus, poorly covered by the scientific literature. The article reviews the state of the art on these new emerging technologies, summarizing how ports and terminals are deploying specific projects in the new era of smart ports and Ports 4.0.
Digital twin technology has a huge potential for widespread applications in different industrial sectors such as infrastructure, aerospace, and automotive. However, practical adoptions of this technology have been slower, mainly due to a lack of application-specific details. Here we focus on a digital twin framework for linear single-degree-of-freedom structural dynamic systems evolving in two different operational time scales in addition to its intrinsic dynamic time-scale. Our approach strategically separates into two components – (a) a physics-based nominal model for data processing and response predictions, and (b) a data-driven machine learning model for the time-evolution of the system parameters. The physics-based nominal model is system-specific and selected based on the problem under consideration. On the other hand, the data-driven machine learning model is generic. For tracking the multi-timescale evolution of the system parameters, we propose to exploit a mixture of experts as the data-driven model. Within the mixture of experts model, Gaussian Process (GP) is used as the expert model. The primary idea is to let each expert track the evolution of the system parameters at a single time-scale. For learning the hyperparameters of the ‘mixture of experts using GP’, an efficient framework that exploits expectation-maximization and sequential Monte Carlo sampler is used. Performance of the digital twin is illustrated on a multi-timescale dynamical system with stiffness and/or mass variations. The digital twin is found to be robust and yields reasonably accurate results. One exciting feature of the proposed digital twin is its capability to provide reasonable predictions at future time-steps. Aspects related to the data quality and data quantity are also investigated.
This paper relates with the assignment of trucks to time slots in container terminals equipped with Truck Appointment Systems. A two-phase approach is provided: first, export and import containers are matched in tuples with a clustering analysis to reduce the number of empty trips and, then, tuples are assigned to time slots to minimize trucks deviation from their preferred time slots and truck turnaround times. Real case instances related to Mexican and Italian container terminals are tested. Results show that our approach reduces empty-truck trips up to 33.79% and that it can be successfully applied to any container terminal.
Predictive maintenance is one of the important technical means to guarantee and improve the normal industrial production. The existing bottlenecks for popularization and application are analyzed. In order to solve these problems, a cooperative awareness and interconnection framework across multiple organizations for total factors that affect prediction maintenance decision-making is discussed. Initially, the structure and operation mechanism of this framework are proposed. It is designed to support the sharing of data, knowledge and resources. As a key supporting technology, the digital twin is also integrated into it to improve the accuracy of fault diagnosis and prediction and support making a maintenance plan with higher accuracy and reliability. Then, under this framework, an integrated mathematical programming model is established with considering the parameter uncertainty and an NSGA-II hybrid algorithm is utilized to solve it. Moreover, an adjustment strategy for a maintenance plan is discussed in response to the dynamic characteristics of the actual maintenance environment. Finally, a case, prediction maintenance decision-making for bearings in grinding rolls of the large vertical mill, is studied. Analysis results verify the advantage of the integrated solving mechanism based on the proposed framework. The framework and integrated decision-making approach can guide the implementation of predictive maintenance with higher accuracy and reliability for industrial enterprises.
Modern manufacturing enterprises are shifting toward multi-variety and small-batch production. By optimizing scheduling, both transit and waiting times within the production process can be shortened. This study integrates the advantages of a digital twin and supernetwork to develop an intelligent scheduling method for workshops to rapidly and efficiently generate process plans. By establishing the supernetwork model of a feature-process-machine tool in the digital twin workshop, the centralized and classified management of multiple data types can be realized. A feature similarity matrix is used to cluster similar attribute data in the feature layer subnetwork to realize rapid correspondence of multi-source association information among feature-process-machine tools. Through similarity calculations of decomposed features and the mapping relationships of the supernetwork, production scheduling schemes can be rapidly and efficiently formulated. A virtual workshop is also used to simulate and optimize the scheduling scheme to realize intelligent workshop scheduling. Finally, the efficiency of the proposed intelligent scheduling strategy is verified by using a case study of an aeroengine gear production workshop.
For dynamic scheduling, which is daily decision-making in a job-shop, machine availability prediction, disturbance detection and performance evaluation are always common bottlenecks. Previous research efforts on addressing the bottlenecks primarily emphasize on the analysis of data from the physical job-shop, but with little connection and convergence with its virtual models and simulated data. By introducing digital twin (DT), further convergence between physical and virtual spaces of the job-shop can be achieved, which greatly enables dynamic scheduling. DT fuses both real and simulated data to provide more information for the prediction of machine availability on one hand; and on the other hand, it helps to detect disturbances through comparing the physical machine with its continuously updated digital counterpart in real time, triggering timely rescheduling when needed. It also enables comprehensive performance evaluation for rescheduling using multiple-dimension models, which can describe geometric properties, physics parameters and behaviors of the machines. In the paper, a five-dimension DT for a machine in the job-shop is introduced first, then the DT-based machine availability prediction, disturbance detection and performance evaluation methods are explored. Based on this, a DT-enhanced dynamic scheduling methodology is proposed. A scheduling process of making hydraulic valves in a machining job-shop is taken as a case study to illustrate the effectiveness and advantages of the proposed method.
Seaborne trade volumes are growing and the resulting traffic load on the road infrastructure in port areas calls for new solutions to improve handling and coordination of vehicles and shipments. In this contribution, a digital twin for truck dispatching operator assistance is presented, which enables the determination of optimal dispatching policies using simulation-based performance forecasts. Proprietary simulation software with limited interfaces, the application deployment in demanding industrial environments, and the integration of real-time sensor information represent three major challenges when realizing digital twin applications for logistics systems. This implementation, therefore, uses an extensible open-source simulation package and it is coupled with an IoT-platform for easy integration of real-time information. The digital twin is deployed as a cloud-based service, allowing for simple deployment and scalability.
This paper aims at delineating major features of the two new perspectives in supply chain (SC) disruption risk management, i.e., ripple effect and resileanness. The methodologies to mitigate the SC disruptions and recover in case of severe disruptions are discussed. It observes the reasons and mitigation strategies for the ripple effect in the SC and presents the ripple effect control framework that is comprised of redundancy, flexibility, and resilience. Even though a variety of valuable insights has been developed in the given area in recent years, new research avenues and ripple effect taxonomies are identified for the near future. The special focus is directed towards the supply chain risk analytics for disruption risks and the ripple effect in digital supply chains. In particular, the digital SC twin framework is presented.
With the continuous development of seaports, problems related to the storage of containers in terminals have emerged. Such problems, referred to in the scientific literature as Container Stacking Problems (CSP) consist in determining the exact location of containers in the storage area of a terminal. Several research works have been conducted to develop systems for the management of container storage operations, which are referred to as Container Terminal Operating Systems (CTOS). Unfortunately, existing systems suffer limitations related to distributed control, online stacking strategies efficiency and their ability to handle dangerous containers. In this paper, we suggest a multi-agent approach for the reactive and decentralized control of container stacking in an uncertain and disturbed environment. A Belief-Desire-Intention (BDI) model has been proposed for the development of the different agents constituting the system. A set of knowledge models and learning mechanisms for disturbance and reactive decision making management are suggested and integrated in the system. The suggested system is able to capture, store and reuse knowledge in order to detect disturbances (as those related to resources breakdown), select the most appropriate storage strategy and determine the most suitable container location.
In a container terminal, the arriving times and handling volumes of the vessels are uncertain. The arriving times of the external trucks and the number of containers which are needed to be brought into or retrieved from a container terminal by external trucks within a period are also uncertain. Yard crane (YC) scheduling is under uncertainty. This paper addresses a YC scheduling problem with uncertainty of the task groups' arriving times and handling volumes. We do not only optimize the efficiency of YC operations, but also optimize the extra loss caused by uncertainty for reducing risk of adjusting schedule as the result of the task groups' arriving times and handling volumes deviating from their plan. A mathematical model is proposed for optimizing the total delay to the estimated ending time of all task groups without uncertainty and the extra loss under all uncertain scenarios. Furthermore, a GA-based framework combined with three-stage algorithm is proposed to solve the problem. Finally, the proposed mathematical model and approach are validated by numerical experiments.
In the considered Automated Container Terminal, twin (i.e., identical non-passing) automated stacking cranes are configured for each block. The twin Automated Stacking Cranes (ASCs) collaborate to serve storage and retrieval requests from opposite ends of a storage block. Since the ASCs are unable to pass each other, there is a handshake area that serves as a temporary storage location so that one crane can start a request and leave it to the other crane to complete it. In this paper, four modes of crane interference are summarized, and a mixed integer programming model is established to minimize the makespan of all requests. For large scale problems, a genetic algorithm is designed. Numerical experiments show that the algorithm is efficient and competitive. A serial of experiments are carried out where it is shown that the method can produce optimum solutions.
This paper introduces a novel optimization problem resulting from the combination of two major existing problems arising at storage yards in container terminals. The Yard Crane Scheduling Problem is typically concerned with routing the crane given a sequence of storage and retrieval requests to perform, while the Container Relocation Problem tackles the minimization of relocations when retrieving containers in a simpler setting. This paper is the first to consider a model that integrates these two problems by scheduling storage, retrieval and relocations requests and deciding on storage and relocation positions. We formulate this problem as an integer program that jointly optimizes current crane travel time and future relocations. Based on the structure of the proposed formulation and the linear programming relaxation of subproblems, we propose a heuristic local search scheme. Finally, we show the value of our solutions on both simulated instances as well as real data from a port terminal.
Semi-open queuing networks (SOQNs) are widely applied to measure performance of manufacturing, logistics, communications, restaurant, and health care systems. Many of these systems observe variability in the customer arrival rate. Therefore, solution methods, which are developed for SOQNs with time-homogeneous arrival rate, are insufficient to evaluate the performance of systems which observe time-varying arrivals. This paper presents an efficient solution approach for SOQNs with time-varying arrivals. We use a Markov-modulated Poisson Process to characterize variability in the arrival rate and develop a matrix-geometric method (MGM)-based approach to solve the network. The solution method is validated through extensive numerical experiments. Further, we develop a stochastic model of the landside operations at an automated container terminal with time-varying truck arrivals and evaluate using the MGM-based approach. Results show that commonly used time-homogeneous approximation of time-varying truck arrivals is inaccurate (error is more than 15 % in expected waiting time and expected number of trucks waiting outside the terminal) for performance evaluation of the landside operations. The application results are insightful in resource planning, demand leveling, and regulating the number of trucks permitted inside the terminal.
With the bottleneck of port operation moving from the quay side to the yard area, storage yard management is becoming increasingly important in the container terminal. This paper studies on storage yard management in container terminal, a flexible yard template strategy is proposed instead of the fixed yard template strategy. Based on the strategy, an integrated optimization model simultaneously considering space allocation and yard crane deployment for the tactical storage yard management is formulated. Besides, Numerical experiments are conduced to verify the effectiveness of the proposed strategy and mathematical model.
This paper investigates replanning strategies for container-transportation task allocation of autonomous Straddle Carriers (SC) at automated container terminals. The strategies address the problem of large-scale scheduling in the context of uncertainty (especially uncertainty associated with unexpected events such as the arrival of a new task). Two rescheduling policies–Rescheduling New arrival Jobs (RNJ) policy and Rescheduling Combination of new and unexecuted Jobs (RCJ) policy–are presented and compared for long-term Autonomous SC Scheduling (ASCS) under the uncertainty of new job arrival. The long-term performance of the two rescheduling policies is evaluated using a multi-objective cost function (i.e., the sum of the costs of SC travelling, SC waiting, and delay of finishing high-priority jobs). This evaluation is conducted based on two different ASCS solving algorithms–an exact algorithm (i.e., branch-and-bound with column generation (BBCG) algorithm) and an approximate algorithm (i.e., auction algorithm)–to get the schedule of each short-term planning for the policy. Based on the map of an actual fully-automated container terminal, simulation and comparative results demonstrate the quality advantage of the RCJ policy compared with the RNJ policy for task allocation of autonomous straddle carriers under uncertainty. Long-term testing results also show that although the auction algorithm is much more efficient than the BBCG algorithm for practical applications, it is not effective enough, even when employed by the superior RCJ policy, to achieve high-quality scheduling of autonomous SCs at the container terminals.
To resolve the problems of operational efficiency, energy consumption and operational cost of an entire container terminal, the yard crane scheduling secures a crucial position during terminal operational process. Accordingly, it is imperative to develop an efficient yard crane scheduling strategy. In this study, the knowledge acquisition was initially conducted. Subsequently, a knowledge sorting process, including the taxonomic tree generation and organization of acquired knowledge, was completed. Afterwards, the rules were extracted for the purpose of yard crane scheduling. Furthermore, a mechanism was deployed for knowledge reasoning. Consequently, a knowledge-based system was established with regard to yard crane scheduling. To this end, a case study was used to illustrate the proposed knowledge-based system.