A method for developing and validating simulation models for automated storage and retrieval system digital twins

PurposeThe digital warehouse management system is an emergence that forms a critical part of the transformation of economic structure in Industry 4.0. In the present business scenario, the warehouse management system encounters a messy layout, poor damage control, unsatisfactory order management, lack of visibility and lack of technological interventions. Digital twin (DT) based warehouse system shows the ontology and knowledge graphs for competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. There remains a lack of clarity on how the DT can be implemented successfully in warehouse management.Design/methodology/approachThe current literature remains largely unstructured and scattered due to a lack of a systematic approach to integrating the research implications and analysis. This paper probes the conceptualization of the DT with the help of theoretical analysis using the systematic literature analysis method.FindingsThe study explores essential concepts such as interoperability and integrability in implementing DT. Further, it analyzes the role of a supply chain control tower (SCCT) in modern supply chain management. A research framework is proposed for practitioners and academicians by incorporating the opportunities and challenges associated with DT implementation. The research findings are mainly threefold: Conceptualization of DT, Featuring SCCT and Exploration of cross-computer platform interfaces, scalability and maintenance strategies.Originality/valueThis study is among the first to analyze and review DT applications in warehouse management. Moreover, the study proposes a theoretical toolbox for the practitioners to successfully implement the DT in warehouse DT-based warehouse management system: A theoretical toolbox for future research and applications.

In this paper, the aerospace mechatronic servo system's digital flexible production line is taken as the study object, and the demand for automated Warehouse Management System (WMS) is proposed. By optimizing the existing process flow and improving the digital flexible production line, WMS can integrate with Manufacturing Execution System (MES) and Warehouse Control System (WCS) respectively. An integrated MES architecture of aerospace mechatronic servo system's digital flexible production line based on automated WMS is proposed.

Complex electronic equipment final assembly workshops face challenges such as high coupling between information and physical systems, difficulty in integrating multi-source heterogeneous data, and frequent dynamic disturbances. These lead to severe information silos, lagging responses in dynamic scheduling, and low management efficiency. Based on Cyber-Physical System (CPS) theory, this paper proposes and constructs a digital workshop integrated application platform featuring a real-time data-driven collaborative scheduling framework integrating Digital Twin (DT) and Multi-Agent System (MAS). Firstly, a unified CPS framework integrating Manufacturing Operations Management (MOM), Supervisory Control and Data Acquisition (SCADA), Warehouse Management System (WMS), Visualization System (VDS), Advanced Planning and Scheduling (APS), and Digital Twin (DT) is established. Secondly, an ontology-based data encoding specification is designed to reduce interface coupling between systems. Furthermore, through the deep integration of DT and MAS, real-time dynamic response under high-frequency disturbances and closed-loop optimization of the assembly process are achieved. Application on a pulse assembly line for complex electronic equipment shows: assembly disturbance response time reduced by 50%, first-pass yield rate of final assembly increased to 99.32%, material distribution and plan matching degree exceeding 99%, average production waiting time reduced by over 30%, significantly enhancing workshop transparency and overall management and control efficiency.

Cyber–physical systems use digital twins to provide advanced monitoring and control functions, including self-diagnosis. The digital twin is often conceptualized as a 3D model, but mathematical models implemented in numerical simulations are required to reproduce the dynamical and functional characteristics of physical systems. In this work, a cyber–physical system scheme is proposed to monitor and diagnose failures. The virtual system, embedded at the supervisory control level, combines concepts from complex networks and hybrid automata to detect failures in the hardware components and in the execution of the sequential logic control. An automated storage and retrieval system is presented as a case study to show the applicability of the proposed scheme. The functional test and the obtained results validate the implemented system that is shown to be capable of fault diagnosis and location in real time. The online execution of the digital twin present several advantages for diagnosing multiple concurrent failures in sensors, actuators, and the control unit. This approach can be incorporate into diverse manufacturing systems.

Digital twin (DT) has attracted a lot of attention from both industry and academia since it was proposed over a decade ago. A DT can be viewed as a virtual implementation of a real physical system (PS) and used as a representation of the PS for various applications. Despite the great potential of DTs in various fields, implementing DTs to obtain the desired functionality is not always straightforward. Specifically, accurate real-time synchronization between the features at a PS and its DT is essential for the DT to represent the PS. In this case, appropriate networking support is a key component to enable future DT development and applications. Currently, the research on DTs from a networking standpoint is still at an early stage, and only limited work has been done on DT implementation in practical systems. To fill this gap, this article investigates networking-related issues for DTs. Based on the existing literature, a feature-based method is provided for describing the desired properties and quality of DTs from the networking perspective. A stage-based implementation framework is presented for creating large-scale DTs for complex PSs by considering various networking constraints. Networking-related challenging issues and open research topics are discussed at the end.

The concept of Digital Twins is still in an infant state of development. Digital Twins are often built as a tool to aid in better understanding of physical systems through simulation. They can be used to visualize information during operations and provide instructions during training or execution of procedures. The use of Digital Twins to test is appealing as it can be done quickly and safely. However, testing without inclusion of the physical system can lead to a reality gap. The reality gap can lead to high risks when applying concepts tested on digital Twins to the physical system directly. Sometimes interaction with the physical system is unfeasible. In this paper, we present an experimental laboratory that we built to provide a platform for the development of high quality Digital Twins through a feedback loop. The physical system is a Palfinger crane. Our replicate physical twin is a Universal collaborative industrial robot model UR16e due to its similar anatomy to the crane. The RoboDK simulation software was used to rapidly develop a digital twin of the UR16e. We demonstrate a solution to the interoperability problem in digital Twins using the monitoring adaptation loop from the Autonomic Adaptation System of the Arrowhead Framework.

The concept of the digital twin can be thought of as a virtual representation of a physical product, engineering system or facility. This paper presents the role of predictive engineering analytics, alongside operating data, in the digital twin. Using case studies, the authors demonstrate how predictive approaches can be developed to provide data where it cannot be measured and predict future operating data to improve performance, life and integrity of equipment, systems and facilities. The digital twin is fundamentally based on data, larger datasets provide greater insight. Sensor and inspection data are critical. However, there are scenarios in which engineers require data where it cannot be measured or requires data that cannot be measured. Engineers require ways to extract this data, this can be done through predictive engineering analytics. Predictive engineering analytics, in the form of science-based simulations, combines multiple approaches, often based on fundamental principles of physics and engineering. This paper will demonstrate how high-fidelity approaches, such as Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD), combined with system-level simulation and reduced-order modelling can work together with field data to provide this data in real-time. Two case studies presented show how combinations of different levels of science-based modelling approaches can help. A subsea thermal digital twin demonstrates how high-fidelity simulations, undertaken during system design, can be the foundation for reduced order system models capable of capturing critical thermal performance in real time; aiding hydrate risk management during operation. The methods used to train the real-time predictive approach are demonstrated. The second case presented focuses on structural integrity of a heat exchanger showing how real-time sensor data can be translated into structural integrity data and insight through simulation. The cases presented demonstrate the value of science-based predictions to generate data that cannot be obtained from operational sensor data alone. The authors aim to show how the predictive element of the digital twin can be first generated during design and evolved into real-time predictive approaches that provide operations with data that cannot be gained from sensors; when and where it is needed. Digital twins typically use physical data, limiting operators of in-field equipment to make operational decisions based on information from sensor locations and historical data alone. This can limit assessment of operational performance and integrity of complex production and process systems. In this paper the authors aim to show how, by combining predictive approaches, at differing levels of fidelity and based on fundamental scientific principles, it is possible to generate the missing data required, in both space and time. This can inform and guide operations; filling the gaps where and when physical data is not available.

Digital Twins (DTs) are gaining interest among urban planners and city authorities, but a notable gap exists in fully understanding and effectively applying them to complex and multifaceted transport planning processes. This review is an in-depth exploration of the promising concept of DTs and its implications for transport planning—its current applications and conceptualisations within the domain, focusing on their distinction from traditional transport models and identifying critical areas for further DT development. The review was conducted per PRISMA guidelines to identify 136 relevant studies from 2000 to 2024. Over three-quarters of the articles were published after 2021, indicating a growing focus on digital twins in transport planning. A classical five-component definition of DT, consisting of physical, digital, data model, service and connection components, was adapted and used to synthesise key components of the DTs presented in the studies. Simulations were the most common “data model,” and 31% of studies do not present insight into what “services” could be derived from proposed DTs, indicating a top-down approach to DT development. A prevalent misapplication of the DT concept was observed where most studies conflated basic digital models or shadows as DTs without fully meeting the criteria of real-time data integration or bi-directional interaction with physical systems. Interestingly, mature DTs with real-time data and bidirectional interaction were associated with smaller geographical systems and shorter time-range predictions. This suggested that maturity in transport planning DTs depends not only on technological capabilities but also on the spatiotemporal dimension of the system. The review concludes by suggesting directions towards a revised DT maturity, highlighting the level of maturity needed for “usable DTs”, and advocating the need for a bottom-up approach to DT development rather than the top-down one currently adopted by most studies.

Mathematization of research is one of the most effective methods of virtual substantiation of foodstuff recipe and technology. This approach allows creating a product that meets consumer's individual needs, i.e. personalized foodstuff (ethnicity, cultural preferences, regional and environmental characteristics, lifestyle), and at the same time reducing the time and cost of decision-making. The article discusses the hypothesis that the “digital twin” of a food product is a virtual model of the product, namely its mathematical model (simulation model). A simulation model is a logical and mathematical description of a food product that is used to conduct a computerized experiment in order to design desired characteristics and properties. The “digital twin” combines all variety of factors from chemical composition, functional and technological properties to organoleptic indicators. The application of the “digital twin” model of the foodstuff will allow: (1) reacting quickly to changes in the composition, properties and types of raw ingredients, (2) adjusting the product recipe in response to changes in consumer preferences, (3) designing products with a given chemical composition, nutritional value and functional orientation, (4) creating functional, specialized products taking into account the metabolism of nutrients (ethnicity, cultural preferences, health status and clinical factors). Products adapted to the needs of small categories of people will help reducing the risks for those who already have diseases, and will meet the needs of those who would like to make their diet more appropriate to individual needs. The proposed approach to creating a model of the “digital twin” of the foodstuff includes several stages. The first stage involves optimization of the nutritional and biological value of the designed product. The second stage is related to designing the food product’s structural forms. But even if the recipe of a food product is optimally selected in the first stage, it does not guarantee its transformation during processing into a stable system with the required structural, mechanical, functional and technological parameters. Evaluation of the developed food product’s efficiency is possible only by analysing numerous and various parameters and indicators. It is convenient to generalize (convolute) many parameters and indicators into a single quantitative dimensionless indicator. To assess the quality and adequacy of the food product, it is suggested to use an integral indicator in the form of additive convolution – the ‘functional’ of the food product quality.

The paper presents an analytical model for travel time evaluation in double-deep, triple-deep, quadruple-deep and fivefold-deep tier-captive shuttle-based storage and retrieval system (SBS/RS). Under the assumption of the deepest location rule for the storage process and the nearest neighbour rule for the retrieval process, the expressions for single-command and dual-command cycle times of the shuttle vehicle and elevator are developed, from which the SBS/RS performance can be estimated. The model based on the probability theory enables the evaluation of all individual times that make up the cycle times and assessment of the average number of totes relocated during a retrieval process. Special attention is devoted to the modelling of the relocation of totes which block access to the requested tote during the retrieval process. The validity of the analytical model is examined via the developed simulation model. The given case study shows that the proposed analytical travel time model results match the results of the simulation model. The presented modelling strategy could be applied to bigger system racking depths following the same modelling logic.

In the control of complex systems, we observe two diametrical trends: model-based control derived from digital twins, and model-free control through AI. There are also attempts to bridge the gap between the two by incorporating learning-based AI algorithms into digital twins to mitigate mismatches between the digital twin model and the physical system. One of the most straightforward approaches to this is direct input adaptation. In this paper, we ask whether it is useful to employ a generic learning algorithm in such a setting, and our conclusion is “not very”. We denote an algorithm to be more useful than another algorithm based on three aspects: 1) it requires fewer data samples to reach a desired minimal performance, 2) it achieves better performance for a reasonable number of data samples, and 3) it accumulates less regret. In our evaluation, we randomly sample problems from an industrially relevant geometry assurance context and measure the aforementioned performance indicators of 16 different algorithms. Our conclusion is that blackbox optimization algorithms, designed to leverage specific properties of the problem, generally perform better than generic learning algorithms, once again finding that “there is no free lunch”. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Digital twins have the potential to improve productivity and quality in complex systems such as manufacturing systems. Their impact on system performance hinges on the accuracy of their digital models around the system’s operating points. Difficult to measure phenomena, such as wear and tear of equipment, however, may cause a mismatch between the digital twin model and the physical system. In this paper, we formalize this problem and compare 16 potential solution strategies under practical aspects. We argue that readily available off-the-shelf blackbox optimization algorithms may prove more useful for this problem, than more recent learning-based approaches. Specifically, gradient-based algorithms will perform best in systems with high-dimensional, continuous, and non-linear performance functions – even in the presence of white measurement noise.

The production of food is highly complex due to the various chemo-physical and biological processes that must be controlled for transforming ingredients into final products. Further, production processes must be adapted to the variability of the ingredients, e.g., due to seasonal fluctuations of raw material quality. Digital twins are known from Industry 4.0 as a method to model, simulate, and optimize processes. In this vision paper, we describe the concept of a digital food twin. Due to the variability of the raw materials, such a digital twin has to take into account not only the processing steps but also the chemical, physical, or microbiological properties that change the food independently from the processing. We propose a hybrid modeling approach, which integrates the traditional approach of food process modeling and simulation of the bio-chemical and physical properties with a data-driven approach based on the application of machine learning. This work presents a conceptual framework for our digital twin concept based on explainable artificial intelligence and wearable technology. We discuss the potential in four case studies and derive open research challenges.

The paper discusses industrial production management in an Industry 4.0 enterprise based on digital twins of manufacturing process and products. The principles of applying digital twins in the production cycle are defined. We present an example structure of an integrated automated production management system that includes the digital twins and a number of other systems. The functions of digital twins in such a system are indicated. The order of formation and development of digital twins in the course of the enterprise design and construction is considered. At the initial stage of collecting requirements and drawing up technical specifications for the enterprise design, we propose to create an approximate simulation model of the production cycle, which allows the feasibility assessment of key requirements. At the subsequent stages of the enterprise life cycle, the simulation model is enriched up to a full-fledged digital twin and augmented with other components of the management system. We show that the digital twin development can be performed at a high level of formal rigor and automation utilizing the mathematical device of category theory. For this purpose, the novel construction called flexible multicomma category is introduced, representing all possible enterprise architecture variants from a given viewpoint in the sense of the ISO/IEC/IEEE 42010 standard. A flexible multicomma is defined by a signature composed of functors that represent the architecture components from the selected viewpoint. We prove that a multicomma of any fixed shape derived from the signature is embedded into the flexible multicomma.

Adoption of digital twins in smart factories, that model real statuses of manufacturing systems through simulation with real time actualization, are manifested in the form of increased productivity, as well as reduction in costs and energy consumption. The sharp increase in changing customer demands has resulted in factories transitioning rapidly and yielding shorter product life cycles. Traditional modeling and simulation approaches are not suited to handle such scenarios. As a possible solution, we propose a generic data-driven framework for automated generation of simulation models as basis for digital twins for smart factories. The novelty of our proposed framework is in the data-driven approach that exploits advancements in machine learning and process mining techniques, as well as continuous model improvement and validation. The goal of the framework is to minimize and fully define, or even eliminate, the need for expert knowledge in the extraction of the corresponding simulation models. We illustrate our framework through a case study.

Digital equivalence is the main objective of Digital Twin (DT)s, and simulation is an integral part. DTs reach beyond traditional simulation with the help of real-time synchronization through Industrial Internet of Things (IIoT) technologies. Simulation supports off-line experimentations and planning, while DTs offer synchronous execution and modification. DTs help to understand “what may happen”. Also, they present “what is happening” and its management methodologies. In this paper, we present building aspects and an integration approach for a Digital Twin based Discrete-Event Simulation model. Our approach utilizes a data-driven agent-based simulation within a DT framework. It presents an integration layer that provides two essential features: reconfiguration and state initialization. It gives simulation models configurability and integrity that are required for operating within a DT. Our proposed approach is presented through a use case of a semiconductor manufacturing system. The proposed integration layer extends the usability of current Discrete-Event Simulation (DES) for a DT within a Cyber-Physical Production System.