This work presents the development and application of an advanced modeling, simulation and optimization-based framework focused on the production process of a basic element of a internal combustion engine which is supplied by a leading factory in the Latin-American market. Lying on the concepts of the process-interaction approach, the principal components available in the discrete event simulation environment SIMUL8 were used to achieve the best representation of this complex manufacturing system.

This research presents optimized maintenance design using simulation to analyze the capability of auto part manufacturing production system. The integration of simulation and optimization is used to identify critical stations, an optimal system design and maintenance scheduling scheme sand evaluates their effects on the overall system performance. Most emphasis is focused on the impact on system by individual station reliability and the fluctuation of maintenance availability. The proposed simulation and optimization for maintenance design is validated through real-life application. This simulation modeling and optimization could help for manufacturing performance improvement.

Èìèòàöèîííîå ìîäåëèðîâàíèå áèçíåñ-ïðîöåññîâ ïðåäïðèÿòèé â BPsim.MAS.

Ðàçðàáîòêà ðàñïðåäåëåííîé ñèñòåìû èìèòàöèîííîãî ìîäåëèðîâàíèÿ ìóëüòèàãåíòíûõ ïðîöåññîâ ïðåîáðàçîâàíèÿ ðåñóðñîâ BPsim.MAS.

Ìåòîä ïðîåêòèðîâàíèÿ èíôîðìàöèîííûõ ñèñòåì ïðåäïðèÿòèé, îñíîâàííûé íà ñåìàíòè÷åñêèõ ìîäåëÿõ ìóëüòèàãåíòíîãî ïðîöåññà ïðåîáðàçîâàíèÿ ðåñóðñîâ è ïðîãðàììíîãî îáåñïå÷åíèÿ.

Àâòîìàòèçèðîâàííîå ïðîåêòèðîâàíèå èíôîðìàöèîííûõ ñèñòåì ïðåäïðèÿòèé â BPsim.

Discrete event simulation (DES) projects rely heavily on high input data quality. Therefore, the input data management process is very important and, thus, consumes an extensive amount of time. To secure quality and increase rapidity in DES projects, there are well structured methodologies to follow, but a detailed guideline for how to perform the crucial process of handling input data, is missing. This paper presents such a structured methodology, including description of 13 activities and their internal connections. Having this kind of methodology available, our hypothesis is that the structured way to work increases rapidity for input data management and, consequently, also for entire DES projects. The improvement is expected to be larger in companies with low or medium experience in DES.

This paper presents a simulation model of a barge transportation system for petroleum delivery within an inland waterway. The simulation is employed as an evaluation model within a decision support system which also includes a criterion model, represented as a decision maker’s utility function, and an optimization procedure which employs scatter search. Variance reduction techniques are also employed in order to improve the accuracy of the estimates of the performance measures associated with the system. The main purpose of the system is to determine values for important inventory policy variables.

In this study, a single-item two-echelon inventory system where the items can be stored in each of N stocking locations is optimized using simulation. The aim of this study is to minimize the total inventory, backorder, and transshipments costs, based on the replenishment and transshipment quantities. To find out the optimum levels of the transshipment quantities among stocking locations and the replenishment quantities, the simulation model of the problem is developed using ARENA 10.0 and then optimized using the OptQuest tool in this software.

This study applies agent-based modeling methodology to investigate individual and social factors underlying inequitable participation patterns observed in a real classroom in which an experimental collaborative activity was implemented. We created agent-based simulations of simplified collaborative activities and qualitatively compared results from running the model with the classroom data.

A simulation was commissioned to understand the interactions that constrain the capacity of a steel plant. The aim was for this to become a reusable tool that could evaluate the effect of future changes to market requirements and operational practices. This paper describes how a simulation model incorporating human decisionmaking was conceived and constructed. The use of simulation as a tool for knowledge capture in scheduling is considered. The resulting tool has been in use for four years and has acted as a driver to reconsider where the real processing bottlenecks are and what part scheduling can play in managing them.

Simulation results are often needed within a short time frame, while the development of simulation models can be time consuming. We develop a methodology to facilitate rapid generation of simulation models from an enterprise database. Data is communicated between PLM software and Flexsim using a standard Microsoft Excel format. We have developed a custom Flexsim interface and software-specific model generator that creates a discrete event simulation model from the PLM input data. Preliminary results show that the methodology can reduce the cost of simulation model generation while simultaneously improving the accuracy of generated models. This work highlights the benefits of automatic model generation techniques, describes a shipbuilding implementation of the methodology, and provides direction for future work.

This paper introduces an application of simulation-based multi-objective optimization to solve a system configuration problem in a hybrid flow shop system. The test case is provided by a firm that manufactures mechanical parts for the automotive sector. We present an architecture that uses both discrete-event simulation and mathematical programming tools in order to solve the problem. The multiple objective nature of the problem is preserved throughout the proposed approach, using Pareto-dominance concepts both to eliminate inefficient solutions within the proposed solution algorithm and to provide the user with efficient solutions. Mathematical programming is used to cull the required number of simulation runs. Computational results obtained using a real-world case study are reported. The proposed approach is benchmarked against a general purpose simulation-optimization engine in order to prove its effectiveness.

To be able to develop reasoning units based on findings from sciences like behaviorism, psychology, neurology, and psychoanalysis, a test bed which is close to these sciences is needed. A possible approach is to use cognitive agents situated within a game of artificial life. Using such a simulated environment reduces the abstraction step from the origin science to the technical system. This article gives a short overview on such a game of life and discusses several design issues. The simulator is implemented using the simulation software AnyLogic.

The short overview of the strategies and action plans in production and use of biofuels in Europe and Latvia is given. The characteristic of biofuel supply chain as a system is explained. Existing solutions in improvement of biofuel logistic systems are analysed as well as available tools for the modelling of biofuel supply chains. As the first system for modelling the pure vegetable oil from rapeseed was chosen and first results of modelling are discussed.

This paper discusses various existing traceability methodologies and describes their application and extension for Model-Driven Performance Engineering by taking its specific needs into account.

We give a tutorial introduction to simulation optimization. We begin by classifying the problem setting according to the decision variables and constraints, putting the setting in the simulation context, and then summarize the main approaches to simulation optimization. We then discuss three topics in more depth: optimal computing budget allocation, stochastic gradient estimation, and the nested partitions method. We conclude by briefly discussing some related research and currently available simulation optimization software.

Simulation is a powerful tool if understood and used properly. This introduction to simulation tutorial is designed to teach the basics of simulation, including structure, function, data generated, and its proper use. The introduction starts with a definition of simulation, goes through a talk about what makes up a simulation, how the simulation actually works, and how to handle data generated by the simulation. Throughout the paper, there is discussion on issues concerning the use of simulation in industry.

This paper describes a simulation study conducted for a company of the German automobile supply industry facing the need to improve delivery reliability. The intention of the study was to evaluate whether Workload Control (WLC) is applicable as production control policy for this company and whether improvement can be expected.

ExtendSim 7 is a proven simulation environment capable of modeling a wide range of systems. ExtendSim 7 is used to model continuous, discrete event, discrete rate, and agent based systems. ExtendSim’s design facilitates every phase of the simulation project, from creating, validating, and verifying the model, to the construction of a user interface that allows others to analyze the system. Simulation tool developers can use ExtendSim’s built-in, compiled language, ModL, to create reusable modeling components. All of this is done within a single, self-contained software program that does not require external interfaces, compilers, or code generators. This paper will introduce ExtendSim 7, demonstrate its advanced technology and features, and explain sample simulation applications.

Techniques are presented for modeling and generating the univariate probabilistic input processes that drive many simulation experiments. Emphasis is on the generalized beta distribution family, the Johnson translation system of distributions, and the Bézier distribution family. Also discussed are nonparametric techniques for modeling and simulating time-dependent arrival streams using nonhomogeneous Poisson processes. Public-domain software implementations and current applications are presented for each input-modeling technique. Many of the references include live hyperlinks providing online access to the referenced material.

In this tutorial we present techniques for building valid and credible simulation models. Ideas to be discussed include the importance of a definitive problem formulation, discussions with subject-matter experts, interacting with the decision-maker on a regular basis, development of a written assumptions document, structured walk-through of the assumptions document, use of sensitivity analysis to determine important model factors, and comparison of model and system output data for an existing system (if any). Each idea will be illustrated by one or more realworld examples. We will also discuss the difficulty in using formal statistical techniques (e.g., confidence intervals) to validate simulation models.

The Monte Carlo and discrete-event simulation code associated with the Simulation 101 pre-conference workshop (offered at the 2006, 2007, and 2008 WSC) is available in both C and R. This paper begins with general instructions for downloading, compiling, and executing the software. This is followed by detailed explanations of two programs that are representative of the software suite: craps uses Monte Carlo simulation to estimate the probability of winning the dice game Craps, and ssq2 uses discrete-event simulation to estimate several measures of performance associated with a single-server queue.

With the progress in modeling dynamic systems new extensions in model coupling are needed. Depending on the general conditions of the system the description of the model and thereby the state space is altered. This change of system behavior can be implemented in different ways. In this work we focus on AnyLogic and its ability to switch between different sets of equations using UML statecharts. Different possibilities of the coupling of the state spaces are compared. This can be done either using a parallel model setup, a serial model setup, or a combined model setup. The analogies and discrepancies can be figured out on the basis of three classical examples.

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, the threat of bio-warfare, and the factors responsible for the fall of ancient civilizations. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing agent models, and illustrates the development of a simple agent-based model.

This paper presents a simulation model of the operation of a liner shipping network that considers multiple service routes and schedules.

Many general-purpose simulation languages (such as Arena, SLAM II, GPSS/H, Siman etc) have been the major simulation tools to simulate the demand-supply processes. They have made great contributions to the decision making. But in the recent years, followed by the fast development of Window applications, powerful PC hardware and software, numerous applications have used different approaches to develop simulation applications. One rapidly developing area in simulation is dynamic data-driven simulation by using data manipulation and analysis packages. SAS is a powerful tool for data analysis and data manipulation. It can also be used to build simulation models in data driven applications. This article presents our research development in this area for demand forecast applications.

The aim of this research is to introduce the reader to a new perspective on the framework for designing a manufacturing line project in Japanese automobile manufacturingplants.

The paper presents the discrete hybrid automata (DHA) modelling formalism and related HYSDEL modelling language. The applicability of the framework in the context of modelling of structural-dynamic systems is discussed. High level and partially modular modelling capabilities of HYSDEL are presented and the possibility of modelling structural-dynamic systems is shown and illustrated by a simple example. To model structural dynamics, standard HYSDEL list structures are employed, and additional dynamic modes are introduced when state re-initializations are necessary at mode switching. For the derived DHA models an efficient simulation algorithm is presented. The main features of the framework are compared to characteristics of other modelling and simulation tools capable of capturing structural dynamics.

In Scicos, a graphical user interface (GUI) has been developed for the initialization of Modelica models. The GUI allows the user to fix/relax variables and parameters of the model as well as change their initial/guess values. The output of the initialization GUI is a pure algebraic system of equations which is solved by a numerical solver. Once the algebraic equations solved, the initial values of the variables are used for the simulation of the Modelica model. In this paper, we present the way the incidence matrix associated with the equations of the system can be exploited to help the user to select variables to be fixed and to set guess values of the variables during the initialization phase.

We discuss methods for statistically analyzing the output from stochastic discrete-event or Monte Carlo simulations. Terminating and steady-state simulations are considered.

This paper describes a new modeling system – Simio that is designed to simplify model building by promoting a modeling paradigm shift from the process orientation to an object orientation. Simio is a simulation modeling framework based on intelligent objects. The intelligent objects are built by modelers and then may be reused in multiple modeling projects. Although the Simio framework is focused on object-based modeling, it also supports a seamless use of multiple modeling paradigms including event, process, object, and agent-based modeling.

Verification & Validation of simulation models and results has been strongly investigated in the context of defence applications. Significantly less substantial work can befound for applications for production and logistics, which is surprising when taking into account the massive impact that wrong or inadequate simulation results can have on strategic and investment-related decisions for large production and logistics systems. The authors have, therefore, founded an expert group for this specific topic in the year 2003, which has analysed the existing material and then developed proposals for definitions, overviews on existing V&V techniques, practical hints for the documentation of the procedural steps within a simulation study, and a specific procedure model for V&V in the context of simulation for production and logistics. The results of this working group are available as a textbook, in German. This paper summarises major results.

Commercial airlines often encounter imbalances in their inventory of unit loading devices (ULDs). A stochastic simulation model was developed to evaluate inventory policies. The structure of the simulation model is described. We evaluate a minimum ULD loading configuration policy and demonstrate how it reduces ULD shortages and helps balance ULD network flow and inventory. As a result, airlines can reduce operating expenses and improve customer service. Finally, we give future directions for studying ULD inventory.

In this paper, «continuous systems with structural dynamics» shall be understood as dynamical systems consisting of components with continuous and/or discrete behaviour. Continuous systems with structural dynamics - or so-called «hybrid systems» - can often be investigated only by a so-called «hybrid simulation» which means a simultaneous simulation of continuous-time dynamics (modelled by differential equations or differential-algebraic equations (DAE)) and discrete-event dynamics. To this end, an algorithm for numerical simulation of hybrid systems must be able to both solve a DAE system within a «continuous» time progression as well as to deal with event-driven phenomena.

Proposed in this paper is the architecture of a PLC programming environment that enables a visual verification of PLC programs. The proposed architecture integrates a PLC program with a corresponding plant model, so that users can intuitively verify the PLC program in a 3D graphic environment. The plant model includes all manufacturing devices of a production system as well as corresponding device programs to perform their tasks in the production system, and a PLC program contains the control logic for the plant model. For the implementation of the proposed PLC programming environment, it is essential to develop an efficient methodology to construct a virtual device model as well as a virtual plant model. The proposed PLC programming environment provides an efficient construction method for a plant model based on the Discrete Event Systems Specifications formalism, which supports the specification of discrete event models in a hierarchical, modular manner.

Building a simulation model for any large complex system requires high expertise and effort. These requirements can be reduced through building generic simulation capability that includes artifacts for facilitating the development of the simulation model. The artifacts can have a range of capabilities depending on the design goals for the simulation. This paper focuses on issues to be considered in building a generic simulation capability for supply chains. A number of approaches used in recent years for building generic supply chain simulation capability are discussed. Such approaches include data-driven simulators, interactive simulators, and sub-models for supply chain components. Tradeoffs are identified that should be considered in selecting an approach for building a generic supply chain simulation capability.

The work performed by the authors to provide to Modelica more discrete-event system modeling functionalities is presented. These functionalities include the replication of the modeling capacities found in the Arena environment, the SIMAN language and the DEVS formalism. The implementation of these new functionalities is included in three free Modelica libraries called ARENALib, SIMANLib and DEVSLib. These libraries also include capacities for random number and variates generation, and dynamic memory management. As observed in the work performed, discrete-event system modeling with Modelica using the process-oriented approach is difficult and complex. The convenience to include a new concept in the Modelica language has been observed and is discussed in this contribution. This new concept corresponds to the model communication mechanism using messages.

This research proposes the integration of a Geographic Information System (GIS) with the Arena Simulation software to model the transit of ocean-going vessels through the Panama Canal. The purpose of this integration is to initialize the simulation model with the vessels that are currently transiting the system and the ones ready to begin their transit taking into account waiting time in queue, booking status, navigation restrictions and their times through the locks.

The paper presents the conceptual mesoscopic model of the defined transport network and its implementation with MS Excel plus VBA. The modelling task is to estimate the dynamics of all the queues and the crossroads capacity utilization. The mesoscopic approach is quite new; there is only one paper, which validates mesoscopic approach, done for queuing systems. That is why the task of mesoscopic models validation is implemented. To validate mesoscopic model we are using simulation on microscopic level, by development of the model in simulation package PTV VISION VISSIM 5.1, which is widely used for traffic simulation.

how it works and why it matters // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.182-192.

This paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation software works. Topics include discrete-event systems; entities, resources, control elements and operations; simulation runs; entity states; entity lists; and entity-list management. The implementation of these generic ideas in AutoMod, SLX, and Extend is described. The paper concludes with several examples of “why it matters” for modelers to know how their simulation software works, including coverage not only of AutoMod, SLX, and Extend, but also of SIMAN (Arena), ProModel, and GPSS/H.

This paper discusses a discrete event simulation model developed to identify and understand the impact of different failures on the overall production capabilities in a chemical plant. The model will be used to understand key equipment components that contribute towards maximum production loss and to analyze the impact of a change policy on production losses. A change policy can be classified in terms of new equipment installation or increasing the stock level for the failure prone components. In this paper, we present the approach used and some preliminary results obtained from available data.

Shresta Sanjiv, Mayer Ralf H. Modeling of air traffic arrival operations through agent-based simulation // Proceedings of the 2008 Winter Simulation Conference (WSC 2008), December 07-10. Miami, USA, 2008. P.2673-2681.

This paper reports on the development and validation of an agent based simulation model of air traffic control arrival operations. The simulation model includes modeling of both the structure and procedures of air traffic operations. It is thus suitable for evaluating the impacts of shifts in those structures and procedures. Three key operational metrics are introduced which are sensitive to the internal workings of air traffic arrival operations. The simulation model is validated by demonstrating agreement in those key metrics between the simulation and a set of baseline arrival operations radar data. After the simulation model has been shown to reproduce actual operations, select details of the simulation can be altered to incorporate proposed operational changes. The impact of the changes on the computer simulation will offer a prediction of howthe operational changes will affect actual operations.

This paper suggests a generic simulation platform that can be used for real-time discrete event simulation modeling. The architecture of the proposed system is based on a tested flexible input data architecture developed in Labview, a real-time inter-process communication module between the Labview application and a discrete event simulation software (in this case Arena). Two example applications in the healthcare and manufacturing sectors are provided to demonstrate the ease of adaptability to such physical system.

In this paper, we address a generalized method of mapping a control system simulation model to the PLC emulator being tested using model variables and PLC tags under the offline commissioning environment. For this research we created an example system similar to a high speed packaging system described in a previous WSC paper. Implementation experience using Rockwell Software applications is provided.

Lightweight agents distributed in space have the potential to solve many complex problems. In this paper, we examine a model where agents represent individuals in a genetic algorithm (GA) solving a shared problem. We examine two questions: (1) How does the network density of connections between agents affect the performance of the systems? (2) How does the interaction topology affect the performance of the system?

Succeeding with a technology as powerful as simulation involves much more than the technical aspects you may have been trained in. The parts of a simulation study that are outside the realm of modeling and analysis can make or break the project. This paper explores the most common pitfalls in performing simulation studies and identifies approaches for avoiding these problems.

This paper describes the optimization of transport solutions using evolutionary algorithms coupled with the simulation model. The vast transportation network in combination with a large number of possible transportation configuration sand conflicting optimization criteria make the optimization problem very challenging. A large number of simulation evaluations are needed before an acceptable solution is found, making the computational cost of the problem severe. To address this problem, a computationally cheap surrogate model is used to offload the optimization process.

IBM General Business Simulation Environment (GBSE) is a supply chain simulation tool developed by IBM China Research Lab. It can capture supply chain dynamics with finest level of granularity and provides great insights to a supply chain’s real operations. GBSE is designed for tactical level decision making; it is proper for supply chain what-if analysis and risk analysis. GBSE implements multiple supply chain processes to considerable details, such as order handling process, inventory control process, manufacturing process, transportation process, procurement process, and planning. The environment is created as a desktop software tool based on Eclipse platform. The backbone framework consists of Presentation Layer, Controller Layer, Service Layer, and Data Layer.

Many studies have examined how learners make sense of the traditionally difficult ideas of levels and emergence in complex systems by interacting with visuospatial multi agent based models. In this poster, we review these findings through the lens of human basic perceptual/representational systems. We argue that many of learners’ observed strategies and explanations surrounding the ideas of levels and emergence are supported by visualizations that leverage perceptual systems related to objects, motion, and geometry.

Mosilab (MOdelling and SImulation LABoratory) a new simulation system developed by Fraunhofer understands Modelica, offers different modeling approaches, and supports structural dynamic systems. This will be discussed on the basis of a main example, the classical constrained pendulum. We show how the solution can be done using only standard Modelica components, where the benefits are and which kind of switching the states can be done. As we will see there is no possibility to define separate submodels with different state space dimensions and switch between these systems during one simulation run.

Current equation-based modeling languages are often confronted with tasks that partly diverge from the original intended application area. This results out of an increasing diversity of modeling aspects. This paper briefly describes the needs and the current handling of multi-aspect modeling in different modeling languages with a strong emphasis on Modelica. Furthermore a small number of language constructs is suggested that enable a better integration of multiple aspects into the main-language. An exemplary implementation of these improvements is provided within the framework of Sol, a derivative language of Modelica.