Related papers: Modelling and Analysis of Supply Chains using Prod…
Current products, especially in the automotive sector, pose complex technical systems having a multi-disciplinary mechatronic nature. Industrial standards supporting system engineering and production typically (i) address the production…
This research focuses on timestamping methods for profiling network traffic in software-based environments. Accurate timestamping is crucial for evaluating network performance, particularly in Time-Sensitive Networking (TSN). We explore and…
Precise time synchronization is expected to play a key role in emerging distributed and real-time applications such as the smart grid and Internet of Things (IoT) based applications. The Precision Time Protocol (PTP) is currently viewed as…
The pursuit of quality improvements and accountability in the food supply chains, especially how they relate to food-related outcomes, such as hunger, has become increasingly vital, necessitating a comprehensive approach that encompasses…
It is well established that resource failure, including robots and machines, in a manufacturing system can result in deadlocks. This issue not only hampers the system's performance but can also inflict significant damage on the…
Many dynamical systems can be successfully analyzed by representing them as networks. Empirically measured networks and dynamic processes that take place in these situations show heterogeneous, non-Markovian, and intrinsically correlated…
The recently introduced formalism of DB-nets has brought in a new conceptual way of modelling complex dynamic systems that equally account for the process and data dimensions, considering local data as well as persistent, transactional…
In the current context of Big Data, the nature of many forecasting problems has changed from predicting isolated time series to predicting many time series from similar sources. This has opened up the opportunity to develop competitive…
Supply chain networks are critical to the operational efficiency of industries, yet their increasing complexity presents significant challenges in mapping relationships and identifying the roles of various entities. Traditional methods for…
We propose a financial liquidity policy sharing method for firm-to-firm supply networks, introducing a scalable autonomous control function for viable complex adaptive supply networks. Cooperation and competition in supply chains is…
Network theory is rapidly changing our understanding of complex systems, but the relevance of topological features for the dynamic behavior of metabolic networks, food webs, production systems, information networks, or cascade failures of…
In the modelling and analysis of large, real systems, the main problem in their efficient processing is the size of the global model. One of the popular approaches that address this issue is the decomposition of such global model into much…
Renewable energy power is influenced by the atmospheric system, which exhibits nonlinear and time-varying features. To address this, a dynamic temporal correlation modeling framework is proposed for renewable energy scenario generation. A…
Temporal networks are a class of time-varying networks, which change their topology according to a given time-ordered sequence of static networks (known as subsystems). This paper investigates the reachability and controllability of…
Process mining has matured as analysis instrument for process-oriented data in recent years. Manufacturing is a challenging domain that craves for process-oriented technologies to address digitalization challenges. We found that process…
Petri Nets (PN) are widely used for modeling concurrent and distributed systems, but face challenges in modeling adaptive systems. To address this, we have formalized "rewritable" PT nets (RwPT) using Maude, a declarative language with…
Cyber-physical systems often encompass complex concurrent behavior with timing constraints and probabilistic failures on demand. The analysis whether such systems with probabilistic timed behavior ad-here to a given specification is…
Spiking neural networks (SNNs), inspired by the spiking behavior of biological neurons, provide a unique pathway for capturing the intricacies of temporal data. However, applying SNNs to time-series forecasting is challenging due to…
Existing model-based processes for embedded real-time systems support the analysis of various non-functional properties, most notably schedulability, through model checking, simulation or other means. The analysis results are then used for…
Network representations can help reveal the behavior of complex systems. Useful information can be derived from the network properties and invariants, such as components, clusters or cliques, as well as from their changes over time. The…