Towards a Simulation Model Complexity Measure

Thompson, J. Scott; Hodson, Douglas D.; Grimaila, Michael R.; Hanlon, Nicholas; Dill, Richard

doi:10.20944/preprints202302.0018.v1

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…In line with previous research [1,7,20,[38][39][40], the findings confirm that sources of complexity originate from the physical and virtual environment as well as the development process.…”

Section: Sources Of Complexity In Digital Twin Developmentsupporting

confidence: 90%

Sources of Complexity in the Development of Digital Twins in Manufacturing

Sanchez de Ocaña,

Bruch,

Aslanidou

2024

Advances in Transdisciplinary Engineering

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Digital twins have emerged as a critical technology to enable smart production. Digital twins can enhance the current production system by optimizing the current setup and facilitating decision-making based on facts rather than gut feeling. Despite the numerous benefits explored, digital twins have faced many challenges in developing and implementing production systems. Their complexity is causing a lack of digital twin implementations in the production system. This complexity can be traced back to physical and virtual entities and the digital twin development process. By conducting a case study in a global manufacturing company, this publication explores the sources of complexity when developing digital twins. The findings are organized around the digital twin development steps and their corresponding complexity. The number of different types of entities being modeled, the choice of the modeling approach, modeling low-frequency events, emergent phenomena, and the unpredictability and variability of the manufacturing process are all examples of structural and dynamic complexity that have been found to impede success in digital twin applications. This research has implications for managers who are involved in the development of digital twins in their organizations. It can help with methodological guidance when dealing with an undefined and complicated process of digital twin development.

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Section: Sources Of Complexity In Digital Twin Developmentsupporting

confidence: 90%

Sources of Complexity in the Development of Digital Twins in Manufacturing

Sanchez de Ocaña,

Bruch,

Aslanidou

2024

Advances in Transdisciplinary Engineering

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Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

2023

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Convenience store chains are many people’s top choice for dining and leisure and have logistics procedures that involve each store receiving multiple deliveries because of the varying delivery periods and suitable temperatures for different goods. The estimated arrival time for each delivery has a huge impact on the route arrangement and convenience store preparation for dispatchers to schedule future deliveries. This study collected global positioning system travel data from a fleet of one of the top convenience store chains in Taiwan between April 2021 and March 2022 and proposed machine learning to establish a model to predict travel times. For unavailable data, we proposed the nonlinear regression equation to fill in the missing GPS data. Moreover, the study used the data between April 2022 and September 2022 with mean absolute percentage error to validate the prediction effects exceeding 97%. Therefore, the proposed model based on historical data and the machine learning algorithm in this study can help logistics fleets estimate accurate travel times for their scheduling of future delivery tasks and arranging routes.

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Comparing the Complexity and Efficiency of Composable Modeling Techniques for Multi-Scale and Multi-Domain Complex System Modeling and Simulation Applications: A Probabilistic Analysis

Wagner

2024

Systems

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Modeling and simulation of complex systems frequently requires capturing probabilistic dynamics across multiple scales and/or multiple domains. Cyber–physical, cyber–social, socio–technical, and cyber–physical–social systems are common examples. Modeling and simulating such systems via a single, all-encompassing model is often infeasible, and thus composable modeling techniques are sought. Co-simulation and closure modeling are two prevalent composable modeling techniques that divide a multi-scale/multi-domain system into sub-systems, use smaller component models to capture each sub-system, and coordinate data transfer between component models. While the two techniques have similar goals, differences in their methods lead to differences in the complexity and computational efficiency of a simulation model built using one technique or the other. This paper presents a probabilistic analysis of the complexity and computational efficiency of these two composable modeling techniques for multi-scale/multi-domain complex system modeling and simulation applications. The aim is twofold: to promote awareness of these two composable modeling approaches and to facilitate complex system model design by identifying circumstances that are amenable to either approach.

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Towards a Simulation Model Complexity Measure

Cited by 3 publications

References 31 publications

Sources of Complexity in the Development of Digital Twins in Manufacturing

Sources of Complexity in the Development of Digital Twins in Manufacturing

Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

Comparing the Complexity and Efficiency of Composable Modeling Techniques for Multi-Scale and Multi-Domain Complex System Modeling and Simulation Applications: A Probabilistic Analysis

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