SIMULATION: Transactions of The Society for Modeling and Simulation International

Zech, Philipp; Jäger, Alexandra; Fröch, Georg; Pfluger, Rainer; Breu, Ruth

doi: 10.1177/00375497241251852pmid: N/A

Digital twins have emerged as highly valuable tools for model-based planning, simulation and optimization over the last couple of years, thereby demonstrating considerable potential for application within the construction industry. The introduction of building information modeling (BIM) has effectively established a standardized approach to representing building models. However, in practice, many of these models currently exhibit limitations as to their quality, specifically concerning the level of detail they encompass. In addition, BIM models too often are locked inside a specific vendor’s tool which readily implies a lack of platform independence, or interoperability, which, however, is essential for facilitating single and regressive, i.e., after a design change, model-based building performance simulations. Model-based engineering has effectively addressed comparable challenges within the domain of software engineering over the past decades by facilitating the integration and interoperability of models from various origins, by capitalizing on model-based tool integration. Prompted by these advantages, this study introduces a model-based tool environment that addresses the aforesaid challenges concerning BIM model quality and interoperability. Taking advantage of our proposed model-based tool environment, we implement an agile, continuous planning process for regressive, model-based building performance simulations, thereby enhancing building energy efficiency planning.

Pilarski, Sebastian; Sidhu, Aman; Varró, Dániel

doi: 10.1177/00375497241299054pmid: N/A

Extraordinary amounts of fresh produce are never purchased and are discarded as waste. Reinforcement learning (RL) could serve as a means to improve business profits while reducing food waste via control of store pricing and ordering decisions. We present a discrete-event-based simulation framework for food retail which simulates wholesaler, store, and customer interactions. This simulator is critical for driving development and testing of future RL methods. It provides an efficient learning feedback system across a wide gamut of possible scenarios, which cannot be replicated from live observations or pure historical data alone. This is crucial as RL agents cannot learn robust decision-making policies without exposure to many unique scenarios. We evaluate our simulator on a demonstrative case generated from historical consumption and price data using a provided methodology for synthesizing daily demand from monthly and yearly stats. In this demonstrative case, we investigate proximal policy optimization, soft actor–critic, and deep Q networks trained with different reward formulations to decrease food waste and improve profits. These RL methods reduced food waste by 78%–92% on average on an unseen 3-year test period as compared to a baseline mimicking typical food retail waste. Compared to a second popular baseline in literature, the best performing RL algorithm was able to improve profits by up to 12.3%.

Talasila, Prasad; Gomes, Cláudio; Vosteen, Lars B; Iven, Hannes; Leucker, Martin; Gil, Santiago; Mikkelsen, Peter H; Kamburjan, Eduard; Larsen, Peter G

doi: 10.1177/00375497241298653pmid: N/A

Establishing digital twins is a non-trivial endeavor especially when users face significant challenges in creating them from scratch. Ready availability of re-usable models, data, functions, and tool assets, can help with creation and use of digital twins. A number of frameworks/platforms exist to facilitate creation and use of digital twins. In this paper, we propose such a platform to manage digital twin assets, create composable digital twins from re-usable assets and make the digital twins available as a service to other users. The proposed platform supports the management of re-usable assets, storage, provision of compute infrastructure, communication, monitoring, and execution tasks. Two case studies are used to demonstrate the capabilities of this platform.

Gil, Santiago; Oakes, Bentley J; Gomes, Cláudio; Frasheri, Mirgita; Larsen, Peter G

doi: 10.1177/00375497241261406pmid: N/A

Digital Twins (DTs) can be constructed for many different applications, leading to substantial differences between different case studies. To be able to learn from the challenges and lessons learned by other DT practitioners, it is important that experience reports be consistent to facilitate comparisons. In this paper, we merge three reference description frameworks for DTs, one generated from a systematic mapping study, one generated from an analysis of experience reports, and one from a systematic literature review, to come up with a unified characterization of DT applications. This analysis has identified six non-overlapping and three cross-cutting characteristics in the reference frameworks. This paper showcases the unified characterization with 21 characteristics to report on a DT case study called the Flex-cell, a manufacturing cell with two robotic arms used for cooperative assembly. The generalizability of this unified characterization is validated using a multi-case approach with another case study in robotics and another in the food industry. We call on the DT community to integrate these systematic reporting principles in their future DT experience reports such that other practitioners can learn from each other more effectively.

AbdElSalam, Mohamed; Bensalem, Saddek; Delacourt, Antoine; He, Weicheng; Katsaros, Panagiotis; Kekatos, Nikolaos; Nolasco Ruiz, Ricardo; Peled, Doron; Ponchant, Matthieu; Ryad, Ismail; Temperekidis, Anastasios; Wu, Changshun

doi: 10.1177/00375497241311617pmid: N/A

Effective management of depth of anesthesia (DoA) is crucial for patient safety in healthcare. Anesthesiologists typically adjust anesthetic dosages to maintain desired sedation, analgesia (pain relief), and muscle relaxation states. In this paper, we present a digital twin (DT) architecture for the formal modeling and verification of an infusion pump controller for DoA management. The DT incorporates a virtual patient model, an autonomous DoA controller adjusting the infusion rate of the anesthetic agent, i.e., propofol, a test-case manager, and a runtime verification monitor. Data exchange occurs via Ethernet frames. Challenges arise from noise in the Bispectral Index monitoring system readings and infusion rate measurements in clinical scenarios. To mitigate noise impact, we design a feedback controller that is robust against noise. We reason about DT performance by evaluating control specifications using a temporal-logic language within the context of our runtime verification tool.

Anyene, Ginikachi; Schultz, Celeste; Nepomuceno, Anthony; Kim, Inki

doi: 10.1177/00375497241283047pmid: N/A

This manuscript aims to show the proof-of-concept for the integration of digital twin (DT) and discrete event co-simulation through its application to forward simulation of physical activities of care providers and robots in an intensive care unit (ICU). Co-simulation focuses on modular integration of heterogeneous simulation components to capture the complexity inherent in systems. Advancing this conception, the proposed co-simulation attempted to narrow gaps with real-world systems by using DT while simultaneously leveraging the forward simulation capacity of discrete event simulation (DES). In particular, we created a co-simulation for human–robot interactions in a simulated ICU facility, which modeled the hospital layout including the facility, robot, avatars for healthcare professionals, equipment, patient bed, and vital signs, which comprise physical twin (PT). In addition, specific patient-care scenarios were defined to drive PT for which the states of interest were represented in the DT, and then used for a dynamic input to the discrete event co-simulation. At the onset of the co-simulation, the parameters needed to run DES were manually configured to align the parameters with those of the DT. For internal validation, this DES was replicated under different work-system policies, and the co-simulation outcomes were interpreted in clinical context. Our co-simulation approach has potential for application to a broader class of problems in healthcare planning and management by supporting informed decision-making. Methodologically, timely information exchanges of PT with DT, and ensuing model updates in the Discrete Event System Specification (DEVS) is a promising approach for co-simulation, to maintain robustness against perturbations to real-world systems.

Kibira, Deogratias; Shao, Guodong; Venketesh, Rishabh

doi: 10.1177/00375497241290242pmid: N/A

Digital twins are poised to improve the engineering, operation, and management of manufacturing facilities. However, implementing digital twins has been a challenge primarily due to a lack of resources and the absence of standardized digital twin frameworks and methods. This paper describes creating a digital twin of a robot workcell using available standards, tools, and methods. The workcell comprises collaborative robot arms, a computer numeric control (CNC) machine tool, and a coordinate measuring machine (CMM). The ISO 23247 standard provides a guideline for building the digital twin. Data are collected from physical devices and the MTConnect standard provides a format for communicating the data between the physical equipment and their digital counterparts. The machine components in the workcell are represented as models in the virtual world. This research shows that the above standards and methods support building a digital twin of a robot workcell. The data collected and communicated can be used to improve workcell functions such as quality management and predictive maintenance.