System and simulation modeling using SYSML

Simulation modeling provides powerful decision support capabilities for production planning in manufacturing systems, yet widespread adoption remains limited by significant barriers in model development and maintenance. Traditional simulation approaches require substantial expertise, time investment, and financial resources, creating accessibility challenges particularly for small and medium enterprises. Current research on automatic simulation generation overlooks the significant potential that generative AI can offer in bridging the gap between natural language expression and executable model creation. This paper presents a novel methodology integrating large language models (LLMs) with automatic simulation model generation (ASMG) to enable direct generation of executable simulation models from natural language conversations. Unlike existing ASMG approaches that require formal specifications or simulation knowledge, our framework reduces programming requirements by allowing users to create functional models through conversational interaction. Our approach leverages natural language processing capabilities to transform user requests into executable simulation models without requiring specialized programming knowledge, enhancing human–machine interaction in production planning environments. The framework implements a four-step process: user intent expression through natural language interfaces, knowledge extraction using structured templates, simulation model construction via object-oriented data-driven components, and simulation execution with comprehensive result analysis for resource utilization, bottleneck identification, and production scheduling optimization. Performance testing demonstrates successful model generation across 18 supported action types with response times ranging from 8 s for simple operations to 5 min for complete manufacturing system creation. By enabling direct conversion of natural language requests into functional simulation models, this LLM-enhanced ASMG approach represents a step toward expanding access to simulation technology for non-expert users while maintaining the analytical power needed for complex manufacturing decision support and production control.

In recent years, the analysis and evaluation of manufacturing systems’ behaviour and their performance became essential. Discrete event simulation (DES) as a digital enterprise technology is an effective tool both in production related decision support processes and in structure or performance analysis of manufacturing systems. Building a DES model of a manufacturing system is a difficult task and requires special competence. Reducing the efforts spent on draft simulation is the aim of an ongoing research which is presented in the paper. The developed framework simplifies and accelerates the process of model building. A production oriented implementation of the ANSI/ISA-95 standard was used by the proposed modelling methodology to define a generic data structure supporting the creation of models without specific knowledge related to the simulation software applied. The data structure enables the development and application of proprietary simulation engines tailored for specific problems. On the other hand, numerous objective tests containing simulation model validation methods are also announced through a well-described example problem. By this method, several validating key performance indicators (KPIs) were tested, and it was indirectly determined whether the simulation model adequately represents the behaviour of the real system. The automatic simulation model generation in our approach enables using different simulation tools and presented through the examples of both commercial and self-developed software.

Model-Driven Performance Engineering enables the automatic generation of simulation models based on business process models and monitored process instance data. When we applied an initial version of our tooling to a number of real world processes, we experienced that we need to support the mapping of monitored process instance data into simulation models under consideration of cases where confidence in these data is low, for instance due to a high variance in monitored resource demands, or a low number of executed process instances. The current paper proposes an architecture which utilizes a decision tree for the intelligent mapping of monitored process instance data into simulation models and, as a by-product, which ranks uncertainties within the imported data.

The automatic generation of simulation models has been a recurring research topic for several years. In manufacturing industries, it is currently also becoming a topic of high practical relevance. A well-known challenge in most model generation approaches is the correct reproduction of the dynamic behaviour of model elements, for example, buffering or control strategies. This problem is especially relevant in simulation-based manufacturing control. In such scenarios, simulation models need to reflect the current state and behaviour of the real system in a highly accurate way, otherwise its suggested control decisions may be inaccurate or even dangerous towards production goals. This paper introduces a novel methodology for approximating dynamic behaviour using artificial neural networks, rather than trying to determine exact representations. We suggest using neural networks in conjunction with traditional material flow simulation systems whenever a certain decision cannot be made ex ante in the model generation process due to insufficient knowledge about the behaviour of the real system. In such cases the decision is delegated to the neural network, which is connected to the simulation system at runtime. Training of the neural network is performed by observation of the real systems decision and based on the evaluation of data that can be gained through production data acquisition. Our approach has certain advantages compared to other approaches and is especially well suited in the context of on-line simulation and simulation-based operational decision support. We demonstrate the applicability of our methodology using a case study and report on performance results.

System models are constructed to design, study, and understand complex systems. According to the systems modeling language (SysML) that is a standard for model-based system engineering, all engineering activities should be performed using a common model. To validate complex system models defined in SysML, simulation is usually employed. There are numerous efforts to simulate SysML models using different simulation methods and tools. However, the efficient support of automated generation of executable simulation code is still an issue tangled by the research community. This paper introduces DEVSys, an integrated framework for utilizing existing SysML models and automatically producing executable discrete event simulation code, according to model driven architecture (MDA) concepts. Although this approach is not simulation-specific, discrete event system specification (DEVS) was employed, due to the similarities between SysML and DEVS, mainly in system structure description, and the mature, yet ongoing research on expressing executable DEVS models in a simulator-neutral manner. DEVSys framework elements include (a) a SysML profile for DEVS, enabling integration of simulation capabilities into SysML models, (b) a meta-model for DEVS, allowing the utilization of MDA concepts and tools, (c) a transformation of SysML models to DEVS models, using a standard model transformation language as query/view/transform (QVT), and (d) the generation of DEVS executable code for a DEVS simulation environment with an extensible markup language (XML) interface. The definition and implementation of DEVSys elements, as well as the process for its application are demonstrated and discussed, with the aid of a simple working example.

The use of systems engineering has proven effective in managing complexity and improving system design. Model based system engineering utilizing the Systems Modeling Language (SysML) especially helps in multidisciplinary environments where engineering data needs to be transformed and integrated between environments. Manufacturing is another discipline same as systems engineering is on active digitalization transformation. In the paper, we propose method and solution to apply Model based system engineering (MBSE) for improvement of process planning of assembly lines leveraging model based approach. SysML modeling and execution enable automation of analysis activities as trade‐off, where the behavior of various assembly scenarios of an assembly line can be captured using SysML behavioral diagrams and compared based on various evaluation criteria. However, relying solely on descriptive SysML system models without integrating the virtual representation of the assembly line is insufficient to verify all aspects of system behavior, such as ergonomics and collision avoidance. The main objective of this work is to present a concept for transforming SysML assembly scenarios into the process and resources models of the computer‐aided design and manufacturing (CAD/CAM) environment. This yields a holistic view that serves as a foundation for further production‐related simulations and analyses, enhancing efficiency, ergonomic design, factory layout, and material flow, ensuring effective assembly workstation design optimized at systems engineering level.

The DONS system, Designer Of Network Schedules, supports the development of timetables by generating regular interval timetables for the Dutch railway network; it is used to plan improved or new train services and to locate bottlenecks in the railway infrastructure. A new simulation module enables the planners to study the networkwide dynamic properties of DONS timetables. The direct link between DONS and this simulation module, enhanced by the re-use of previously defined DONS concepts and the automatic generation of simulation models from existing data, facilitates an integrated approach towards infrastructure and railway services development, focused on quality improvement.

The paper is oriented to application of locally used formal theories as tools for analysis and modeling of anticipatory systems, and especially of nesting anticipation, which takes place e.g. in anticipation during a design of a system that is expected to be anticipatory one. The specialization of theories enables to “tailor” them near to the studied systems and the nesting of theories enables formulating exact theories of systems, the elements of which carry their proper theories. In certain programming languages such theories make easy computer model construction and especially automatic generation of simulation models.

The design of production and logistic systems is a process of managing both technical and organisational variants in order to identify the best solution for a given system. This paper discusses design issues of production systems that are applied to an internal logistic system in the automotive industry. As far as the production systems design (PSD) is concerned, three basic classes of software tools usually pertain: computer-aided design (CAD), process simulation, and information systems. However, these software tools have been used with low levels of integration. Vik et al. (2010b, 2010c) proposed integrating these software resources in production systems and developed an advanced tool called integrated design of systems (IDS). The proposed IDS tool involves a wide set of functions for the most common tasks of PSD, from conceptualisation to implementation, including systems analysis (P-Q, cluster, and material flow analysis), automatic generation of simulation models, generation of alternatives for the layout of facilities and factories, material flows display, transportation system design, and iterative buffer size specification. The IDS approach takes advantage of simulation, CAD systems, and their integration. This paper will demonstrate the concept and functionalities of the proposed tool in a real industrial case study.

Simulation languages develop and evolve over time, and the courses in which modeling and simulation are taught must also evolve to keep pace. In the McMaster University MBA program, a study was undertaken in order to select a new simulation language for a system modeling and simulation course in which GPSS had previously been used, in order to demonstrate a broader possible selection of possible application areas for simulation. The language which was selected was SLAM II, and this selection was confirmed by monitoring the reactions and perceptions of the students to both the old and new languages, which were taught concurrently in two separate sections.

An Integrated Intelligent Modeling and Simulation Language for Model-based Systems Engineering

Integrating Analytical Models with Descriptive System Models: Implementation of the OMG SyML Standard for the Tool-specific Case of MapleSim and MagicDraw

One approach to tackle the complexity in the development of cyber-physical systems is system decomposition and modelling in a central system model with model-based system engineering (MBSE). However, MBSE also requires increased development effort due to the high degree of formalisation of the development results using the Systems Modelling Language (SysML). One solution is reusing system elements from model libraries, which requires formalization that allows them to be networked into consistent models. Due to the abstract nature of SysML, the options for formalizing system elements are extremely diverse, meaning that even identical technical system elements are modelled with a different structure by engineers. As a consequence, no standardized structure is maintained and the reuse of system elements from model libraries is very unlikely. This leads to high, redundant modelling efforts as well as a considerable error potential and significantly hinders the economic application of MBSE in mechanical engineering. A standardized structure of the system elements can be ensured through MBSE methodology specific modelling guidelines. Current modelling guidelines for MBSE are based on the SysMLv1 language standard and cannot be used unchanged in SysML v2. Nevertheless, SysML v2 offers great potential for modelling reusable system elements, especially with its synonymous textual and graphical modelling capabilities, enabling the easy linkage of knowledge from various domain-specific tools. Consequently, modelling guidelines based on SysML v2, which guarantee a standardized structure, are required to fully exploit the potential of model libraries in MBSE. To solve this deficit, this paper presents modelling guidelines based on SysML v2 and their application for building reusable system elements.

The development process of complex systems needs to take in account differents domains and aspects. SysML (Systems Modeling Language) is a new modeling language that allows a system description with various integrated diagrams (as structure, behavior and requirements diagrams), but SysML lacks formality for the requirement verification. The aim of this paper is to propose an approach to verify complex systems using SysML as a language which describes the system structure and requirements. Petri nets and temporal logic LTL are used respectively to formalize the system behavior and requirements. The benefit of such formalization is to allow an automatic formal verification. In order to demonstrate this methodology, it will be used a factory automation system, modeled by SysML and Petri nets, and verified by the TINA toolbox.

Under the complex operational environment of the antimissile operation, the application of Systems Modeling Language (SysML) parametric model in weapon system effectiveness evaluation is explored. The function of parametric graph model, the construction process of the model and the optimization method of the model are researched. Using feedback mechanism to collect, collate and calculate the parameters of the model, and realize the leap from the index relationship description to the calculation of the index. Finally, the feasibility and effectiveness of the proposed method is verified by using the system of antimissile weapon system as an example.