Search results

In the last 10 years simulation has become an integral tool in planning and running complex logistic systems. In the future the application of simulation in logistics will increase still further as discussed by Axel Kuhn and Rolf Schmidt of the Fraünhofer Institute for Transport Engineering and Physical Distribution.

Manufacturing systems design is a complex task and is crucial to the future of a company. Computer simulation provides an efficient and visual method for system designers. The popularity of simulation is owing to its ability to model systems in a fast and cost‐effective way, its flexibility and its ability to model the time dynamic behavior of systems. This paper demonstrates the use of computer simulation as a tool for assisting managers and engineers in the design and analysis of a new manufacturing system by presenting a case study. The case study is a study of proposed instrument panel/crashpad production and assembly work‐cells in an automotive industry. The emphasis on the case study was to evaluate alternative designs, predict system performance, detect any potential problems, experiment with system parameters and determine the sensitivity of the system to these parameters. The case study results show that computer simulation can be a useful decision‐making support tool for the analysis of the system performance and the selection of the design alternatives before the system is actually implemented.

Complex system modeling requires not only understanding of modeling framework but also domain knowledge of the system. The purpose of this paper is to present an approach which separates the domain knowledge from the modeling framework with different views.

By establishing the mechanism of association and fusion among the views, the description and characterization of system from different aspect and point of view can form a complete system model. Based on the approach, a modeling and simulation (M&S) platform named SimFaster is developed. Modeling environment and simulation engine are the most important parts of the platform. The modeling environment provides multi‐views and multi‐layers to help the developers to modeling the structure, layers, composition, behavior, and interactions of an application system. The simulation engine provides mechanism of integration and interaction for components and objects, and provides runtime support for the concepts and terms from modeling environment. The simulation engine organizes the objects in the memory of distributed system as reflective object database system, so it is repository centered architecturally.

Based on the approach of multi‐views modeling, the platform is a flexible framework and supports top‐down design, model reuse and interoperation, dynamic refinement of models, corporative design among different users in different stages, and the rebuilt of application rapidly.

This paper deals with high‐level models of the complex systems.

This platform helps to design, modeling, and simulation complex system (especially for weapon combat system). It can participate into all the stages of the development of complex product/system, and can support the validation, refinement, optimization of models, and systems.

This paper presents a multi‐views modeling approach for the modeling of complex system.

The development of computer technology, artificial intelligence, and simulation modelling has become increasingly complex, and yet the application of these techniques is necessary for a company to be effective and competitive. However, the end users may not have the necessary sophistication to apply these technologies effectively on their own. Combining these technologies to provide intelligent interfaces may be beneficial to the non‐expert user. Discusses a conceptual knowledge‐based simulation system and illustrates its applicability using a hypothetical manufacturing example. The example focuses on interfacing knowledge from an expert with a simulation model to make scheduling decisions in a manufacturing environement. The knowledge system is constructed using the M.1 expert system package and the simulation is performed using SLAM II.

This paper aims to address a central concern in modeling and simulating electric grids and the information infrastructure that monitors and controls them. The paper discusses the need for and methods to construct simulation models that include important interactions between the physical and computational elements of a large power system.

The paper offers a particular approach to modeling and simulation of hybrid systems as an enabling technology for analysis (via simulation) of modern electric power grids. The approach, based on the discrete event system specification, integrates existing simulation tools into a unified simulation scheme. The paper demonstrates this approach with an integrated information and electric grid model of a distributed, automatic frequency maintenance activity.

Power grid modernization efforts need powerful modeling and simulation tools for hybrid systems.

The main limitation of this approach is a lack of advanced simulation tools that support it. Existing commercial offerings are not designed to support integration with other simulation software products. The approach to integrating continuous and discrete event simulation models can overcome this problem by allowing specific tools to focus on continuous or discrete event dynamics. This will require, however, adjustments to the underlying simulation technology.

This paper demonstrates an approach to simulating complex hybrid systems that can, in principle, be supported by existing simulation tools. It also indicates how existing tools must be modified to support our approach.

This paper focuses on a conceptual methodology for an integrated simulation model dubbed as dynamic simulation modelling system (DSMS) for proactive and optimal decision making within a project management framework. Due to the uncertainties in project environment, the technical and operational functionality of a facility needs to be assessed during development and operation phases of the project. The simulation model is used for optimising the investment decisions vis‐à‐vis evaluation of functionalities on project facilities in early stages of the project. Project life cycle objective functions (LCOFs) are employed as a set of decision criteria throughout the project’s life. The discussion is being extended on the need for setting up an integrated and user‐friendly model to encompass the processes in the entire life cycle of the project. Details of the system are described and a hypothetical case study is used to demonstrate its capabilities. Possible extensions are then outlined. The C++ programming language in association with the object‐oriented database management system is used to achieve the aforementioned objectives.

To develop a methodology to augment enterprise resource planning (ERP) systems with the discrete event simulation's inherent ability to handle the uncertainties.

The ERP system still contains and uses the material requirements planning (MRP) logic as its central planning function. As a result, the ERP system inherits a number of shortcomings associated with the MRP system, including unrealistic lead‐time determination. The developed methodology employs bi‐directional feedback between the non‐stochastic ERP system and the discrete event simulation model until a set of converged lead times is determined.

An example of determining realistic production lead‐time data in the ERP system is presented to illustrate how such a marriage can be achieved.

The research demonstrates that the limited planning functionality of the ERP system can be complemented by external system such as discrete event simulation models. The specific steps developed for this research can be adopted for other enhancements in different but comparable situations.

The organizations who have been using the discrete event simulation in their planning and decision‐making processes can integrate their simulation models and the ERP system following the steps presented in this paper. The ideas in this paper can be used to look for automatic data collection process to update or build the simulation models.

The ERP implementation is a significant investment for any corporation. Once the ERP implementation is completed successfully, the corporations must look for ways to maximally return on their investment. The research results may be used to enhance the implemented ERP systems or to fully utilize the capabilities in a corporation.

This paper aims to propose a theoretical decision support framework, which integrates artificial intelligence (AI), discrete-event simulation (DES) and database management technologies so as to determine the steady state flow of items (e.g. fixtures, jigs, tools, etc.) in manufacturing.

The existing literature was carefully reviewed to address the state of the arts in decision support systems (DSS), the shortcomings of pure simulation-based and pure AI-based DSS. A conceptual example is illustrated to show the integrated application of AI, simulation and database components of the proposed DSS framework.

Recent DSS studies have revealed the limitations of pure simulation-based and pure AI-based DSS. A new DSS framework is required in manufacturing to address these limitations, taking into account the problems of flowing items.

The theoretical DSS framework is proposed using simple rules and equations. This implies that it is not complex for software development and implementation. Practical data are not presented in this paper. A real DSS will be developed using the proposed theoretical framework and realistic results will be presented in the near future.

The proposed theoretical framework reveals how the integrated components of DSS can work together in manufacturing in order to determine the stable flow of items in a specific production period. Especially, the integrated performance of case-based reasoning (CBR) and DES is conceptually illustrated.

This paper aims to highlight the complex nature of automated guided vehicle (AGV) simulation model building, and especially how system modelling details affect the end results. This is an important issue in all of the transportation simulation systems, since they are service‐based by their nature, and additional inefficiencies create unanticipated performance downgrading.

This paper uses a simulation approach, and simulated systems are based on a real‐life case study and on well accepted hypothetical simulation example.

Simulation system boundaries are often neglected in the model building, and especially interface to inbound (and possibly outbound) material flow should be considered carefully; based on these research results, AGV investments are seen in an entirely different light, as system boundary is enlarged to contain more realistically interacting elements. Similar system boundary issues were found from the case study: interface with overhead gantry did not provide near optimal performance. The case study also revealed that high speed of AGVs is not necessarily worth additional investment; constraints exist in safety, acceleration and ability to turn in corners.

The findings are based on the simulation work and, to see the real implications, real‐life implementations on policy level are needed.

Results of this research provide more insights for manufacturing unit investments, and especially in the scope of automated transportation system use. Also changes in manufacturing flow management issues, after investing in, for example, AGV systems, are different from in less‐automated manufacturing units.

This research work provides more insights to simulation research work, especially from the perspective of transportation systems. Also implications arising from case study are unique as being compared to previous research in the field.

One of the most challenging problems facing industrial engineers concerns the design and operational planning of today′s sophisticated production systems. The need for a detailed quantitative analysis is far more apparent than ever before. The application of discrete‐event simulation has been growing rapidly in the analysis of production systems. This is because no other quantitative methods can provide the flexibility, realism and predictive accuracy offered by the simulation technique. Although the important role that simulation can play in analysing production systems has now been generally realised, its use is not necessarily straightforward. The successful implementation of simulation projects usually depends on several factors which include, inter alia, the availability of simulation expertise and the ability of the available simulation software to model readily and accurately the environment under consideration. The areas of production systems where simulation can be applied are outlined. The essential considerations which must be studied when applying simulation are also discussed. An overview of simulation modelling environments that are currently used is then taken. Recommendations for future work of importance from the system analysis viewpoint are highlighted.