Guided Uncertainty Reduction in Automatically Generated Business Simulations

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.

Simulation languages and the GUIs supporting them may be excellent tools for creating simulation codes, but are not necessarily the best tools to use for creating descriptions of systems, i. e., for modeling. In 2006, OMG published the initial standard specification (OMG 2006) for SysML (Systems Modeling Language), an extension of UML (OMG 2007) designed specifically to support systems engineering. SysML shows great promise for creating object-oriented models of systems that incorporate not only software, but also people, material, and other physical resources, expressing both structure and behavior for such systems. In this paper, we explore the use of SysML both to model a system to be simulated and to support the automatic generation of simulation models.

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.

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.

The main purpose of the research was to discover an easy fast and reliable way to generate simulation models and to create a model library. To solve this problem, the automatic element routine generation system (AERO) has been developed as an efficient way for automatic model development using domain specific automatic programming techniques. Behavioural and structural models are generated from Boolean equations, truth tables, HDL descriptions, schematic diagrams or incomplete specifications. By utilising this approach, simulation automation can be achieved by providing the same environment for model generation and simulation. Results show that the system could greatly reduce the cost of simulation model generation for CAD systems and consequently reduce the design cycle considerably.

Organizations need to be agile and flexible to meet the continuous changes. Business Process Management (BPM) is harnessing the continuous changes suffered by organizations in the value chain and, therefore, in their processes. Simulation models offer the ability to experience different decisions and analyze their results in systems where the cost or risk of actual experimentation are prohibitive. BPMN models are not directly executable nor is it possible to simulate their behavior in various input parameters. This paper proposes the application of model-driven engineering (MDE) to integrate the definition of business processes with Discrete- Event Simulation (DES) as a tool to support decision-making. We propose a platform independent DES metamodel and a set of rules, to automatically generate the simulation model from BPMN 2.0 based business process in accordance with previous metamodel.

This paper presents methods for automatic generation of models of Transducer, a highly flexible communication module for interfacing Multiprocessor System-on-Chip (MPSoC) components. We describe the transducer architecture, comprising the bus interface, high-level communication controllers and buffer management blocks. The well-defined architecture of the transducer enables automatic generation of its Transaction-level and Register-transfer level (RTL) models. Moreover, the simple interface of the transducer provides for a well-defined software interface, making it easy to update the software after changes in MPSoC platform. Our experimental results show that MPSoC design for industrial-size applications, such as MP3 decoder and JPEG encoder, greatly benefits from automatic generation of transducer models. We found productivity gains of 9-23× due to significant savings in modeling effort. On the quality axis, we show that MPSoC communication design using automatically generated transducers has very little overhead in communication delay over a fully connected point-to-point communication architecture. Finally, we show that our automatically generated TLMs greatly reduce the system-level modeling time and provide a fast executable model for early functional validation.

Traditional test case generation approaches focus on design and implementation models while a large percentage of software errors are caused by the lack of understanding in the early phases. One of the most important models in the early phases of software development is business process model which closely resembles the real world and captures the requirements precisely. The aim of this paper is to present a model-based approach to automatically generate test cases from business process models. We first model business processes and convert them to state graphs. Then, the graphs are traversed and transformed to the input format of the “Spec explorer” tool that generates the test cases. Furthermore, we conduct a study to evaluate the impact of process characterizations on the performance of the proposed method.

A software architecture for the virtual construction of structures

Future embedded system products, e.g. smart hand-held mobile terminals, will accommodate a large number of applications that will partly run sequentially and independently, partly concurrently and interacting on massively parallel computing platforms. Already for systems of moderate complexity, the design space will be huge and its exploration requires that the system architect is able to quickly evaluate the performances of candidate architectures and application mappings. The mainstream evaluation technique today is the system-level performance simulation of the applications and platforms using abstracted workload and processing capacity models, respectively. These virtual system models allow fast simulation of large systems at an early phase of development with reasonable modeling effort and time. The accuracy of the performance results is dependent on how closely the models used reflect the actual system. This paper presents a compiler based technique for automatic generation of workload models for performance simulation, while exploiting an overall approach and platform performance capacity models developed previously. The resulting workload models are experimented using x264 video and JPEG encoding application examples.

Automatic layout generation of robotic production cells in a 3D manufacturing simulation environment

3D building models are important in several fields, such as urban planning and roof report for insurance industries. However, enormous time and labor has to be consumed to create these 3D models, using a 3D modeling software such as 3ds Max or SketchUp. In order to automate laborious steps, a GIS and CG integrated system is proposed for automatically generating 3D building models, based on building polygons (building footprints) on digital maps. Digital maps show most building polygons' edges meet at right angles (orthogonal polygon). In the digital map, however, not all building polygons are orthogonal. In either orthogonal or non-orthogonal polygons, the new system is proposed for automatically generating 3D building models with general shaped roofs by straight skeleton computation. In this paper, the algorithm for shrinking a polygon and forming a straight skeleton are clarified and, the new methodology is proposed for constructing roof models by assuming ‘the third event’ and, at the end of the shrinking process, the shrinking polygon is converged to ‘a line of convergence’. In our research, extended straight skeleton computation is used for automatic generation of roof models. Based on the monotone polygons which straight skeleton computation forms, roof boards are automatically generated, and a top view of these roof boards can be a roof report for natural disaster damage.