Extraction of Behavioural Requirements for Simulation-based Performance Evaluation of Manufacturing Systems

When designing simulation models of large-scale logistics systems in manufacturing, such as automotive industry or engineering and petrochemical production, creators of these models consider how to properly render the whole system in one simulation model as accurately as possible. One possible approach to designing such models is the application of a hierarchic structure. The structure groups and combines the essential elements of simulation models into larger units. The aim of the paper is to analyse and identify the potential of the computer simulation using a hierarchic structure for increasing the effectiveness of designing large-scale logistics systems in manufacturing. A case study from the automotive industry using EXTENDSIM software environment is used for that purpose.

The Importance of Cobot Speed and Acceleration on the Manufacturing System Efficiency

Practical Aspects of Designing a Human-centred AI System in Manufacturing

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.

This paper describes an application of simulation modeling to enhance system performance in the semi-automated lamps manufacturing industry with a recently developed multiattribute decision analysis. Multiattribute decision techniques seem to provide an easily understood, yet comprehensive, set of quantitative and qualitative approaches to justify advanced manufacturing systems. Much has been written about the use of multiattribute decision application models to evaluate manufacturing technologies. Simulation modeling approach is developed for planning and scheduling of a semi-automated manufacturing system, hence the qualitative and quantitative multiattribute factors of the manufacturing system are evaluated. A new proposal is developed to achieve a high level of productivity of plant and minimize the makespan and cost of production. An experimental comparison is performed on the effectiveness of the proposed system through a real life case study.

Chapter 9 - Manufacturing systems

The maintenance in manufacturing systems has been widely studied to improve equipment reliability, increase the productivity, and reduce operational cost. Recently, with the increasing concerns on climate change and environmental protection, the energy related performance of manufacturing system has also draw wide attention from both academia and industry. One common decision of maintenance and energy management can be the identification of the machines that could be shut down in the manufacturing system for either maintenance or energy saving purpose. Thus, the idea of implementing maintenance and energy control simultaneously has emerged. Some existing cases about the joint maintenance and energy control in manufacturing systems can be found in literature. In this paper, we further analyze the existing opportunities for joint energy and maintenance decision making in manufacturing systems. A few research directions towards this goal are proposed and discussed. The corresponding research challenges are also analyzed. A numerical case based on a section of an automotive assembly line is used to illustrate the potential benefits of the proposed approach.

Digital Twin-based bottleneck prediction for improved production control

When developing a new manufacturing system or reconfiguring an existing system, it is desirable to have a simulation model for test and evaluation. However, there is often a disconnect between the real system and the simulation model; it is difficult for them to have exactly equivalent behavior. The highest-fidelity “model” is always the system itself. In this paper we propose a framework in which modular models of the manufacturing system components (robots, machines, conveyors, controllers) can be interchanged with their real counterparts, forming a hybrid process. We focus on both the connections between components and the most pertinent aspects of the processed parts. The transfer of parts between the real and virtual domains is particularly challenging; we describe how parts can transition between real and virtual without making substantial changes to the system itself. We discuss how the proposed hybrid process simulation can be used for the design of new manufacturing systems. As the new machines and components are built and installed, they can be “swapped” in for the virtual machines, and testing can be done incrementally. We also discuss how the proposed hybrid process simulation can be used for the upgrade or reconfiguration of existing manufacturing systems. When a new machine or cell is added, or the part flow is reconfigured, the relevant new parts of the system can first be built in simulation and tested as part of the hybrid process, with the new machines. A case study describing the implementation of the hybrid process simulation on the Reconfigurable Factory Testbed at the University of Michigan is presented.

The development and revision of milling machines and components are continuous. With the help of computer technology, we can simulate some activities in a manufacturing system. The main purpose of a simulation is to understand and imitate the behavior of a particular manufacturing system on a computer, before hardware creation, thereby reducing the amount of testing and experimentation in the field. By using a virtual system, less material is wasted and the constraints on the actual operation of the machine in the field can be minimized. In the field of milling machinery, various modeling and simulation methods have been introduced so far. Today, the manufacture of a machine tool can no longer use the time-consuming and costly manufacturing and testing of physical prototypes to detect weak points and then optimize the design. In contrast, the current machine tool design process uses "virtual prototyping" technology to reduce the cost and time of hardware testing and iterative improvements to physical prototypes. The model of a milling machine can be simulated in Matlab simulations through analytical methods using various compositions. The presented virtual modeling methods and milling machine tool simulations make it possible to design, perform complex analyses, test, optimize, and use various types of control structures. in the computer simulation.
 Keywords: Milling Machine, CNC Miling Machine, Matlab

When we build a factory, we construct a building, purchase and install some amount of equipment, and hire and train some number of personnel. When we mix the equipment and the personnel, some behavior emerges and it is very difficult to predict what this behavior will be. In part, this is because the equipment will exhibit less than perfect availability due to pseudo-random breakdowns. But a major contribution to this unpredictability is the fact that humans who have been exposed to the same training will behave differently on the factory floor. In fact, the same people will behave differently over time given their ability to learn from experience. Clearly the behavior of the manufacturing system will depend on the integral of the behavior of the individuals and the interactions between the individuals, and will change with time. Current discrete event simulations of manufacturing are equipment-centered and can be run without any modeling of floor personnel since the equipment models include decision making capability. This is obviously not an accurate reflection of the manner in which current manufacturing systems operate. What is needed is a simulation that includes both equipment and personnel, and includes them with accurate emphasis. This paper describes a prototype simulation for a very small, but relatively complete factory. Equipment is modeled in objects that can accept commands and issue status while personnel are modeled in objects that can accept status and issue commands. An information system connects these two distinct types of objects. We demonstrate that the details of human behavior are as important as the details of machine behavior to factory performance.

Combining Discrete-Event Control and Simulation-Based Monitoring for Supervisory Functions

The search for operational excellence includes both a thorough understanding of the nature of the manufacturing operations and a choice of the right production and inventory control system for the environment in which it will operate. Understanding the nature of manufacturing operations can be facilitated by simulating the manufacturing system, so that the interrelationships among parameters can be studied. Production and inventory control systems can be simulated together with the physical manufacturing environment in order to enhance the understanding of the behaviour of a particular control system and also to facilitate the selection of control systems for the manufacturing system under study. In this paper we report on the design and learning effects of using simulation for investigating the behaviour and impact of different production and inventory control systems in a manufacturing system. We discuss the structure and simulation perspectives of production and inventory control systems. We provide a review of the benefits of using simulation for learning the manufacturing environment, and the related testing of alternative control systems.

There is a growing recognition that current manufacturing enterprises must be agile, that is, capable of operating profitably in a competitive environment of continuously changing customer demands. The use of a virtual enterprise (VE) is becoming increasingly prevalent, and that has been made possible, in part, due to the significant advances in communication and information technology in recent years. A manufacturing system is one of the competitive factors that forms an effective VE. Therefore, for manufacturers wishing to obtain a contract in VE, it is crucial to present attractive and competitive offers to other coalition members. There are several criteria to these offers, such as cycle time, fulfilment of due date or quick shipping date, cost, and quality assurance for the ordered products. Manufacturing system simulation could endorse the basic estimates of the criteria for the strategic offer. The distributed simulation model concept provides practical solutions to facilitate such a large-scaled precise simulation model in the VE environment, because it is constructed as the integration of several manufacturing simulation models of each production module in the factory. As a solution to realize this integration, we propose a Distributed Virtual Factory (DVF) concept that consists of distributed precise simulation models connected by several synchronization mechanisms named Time Bucket algorithms. A DVF enables precise evaluations of the whole manufacturing system, especially in terms of the material flows. In this study, we introduce an Activity Based Costing (ABC) method into the DVF architecture to estimate the detailed cost analysis of the products. The methodology facilitates strategic enterprise management to prepare the request for the bids in the VE environment. The effectiveness of the proposed concept in agile manufacturing is discussed with simulation experiments.

The Development of Simulation Model for Self-reconfigurable Manufacturing System Considering Sustainability Factors