Business Process Reengineering in the Automotive Area by Simulator-Based Design

The automotive industry is facing economic and technical challenges. The economic situation calls for more efficient processes, not only production processes but also renewals in the development process. Accelerating design work and simultaneously securing safe process outcome leads to products in good correspondence with market demands and institutional goals on safe traffic environments. The technique challenge is going from almost pure mechanical constructions to mechatronic systems, where computer-based solutions may affect core vehicle functionality. Since subcontractors often develop this new technology, system integration is increasingly important for the car manufacturers. To meet these challenges we suggest the simulator-based design approach. This chapter focuses on human-in-the- loop simulation, which ought to be used for design and integration of all car functionality affecting the driver. This approach has been proved successful by the aerospace industry, which in the late 1960s recognized a corresponding technology shift.

Model-Based Simulation to Examine Command and Control Issues with Remotely Operated Vehicles

With increased interest in the overall employment of pilotless vehicles functioning in the ground, air, and marine domains for both defense and commercial applications, the need for high-fidelity simulation models for testing and validating the operational concepts associated with these systems is very high. This chapter presents a model-based approach that we adopted for investigating the critical issues in the command and control of remotely operated vehicles (ROVs) through an interactive model-based architecture. The domain of ROVs is highly dynamic and complex in nature. Hence, a proper understanding of the simulation tools, underlying system algorithms, and user needs is critical to realize advanced simulation system concepts. Our resulting simulation architecture integrates proven design concepts such as the model-view-controller paradigm, distributed computing, Web-based simulations, cognitive model-based high-fidelity interfaces and object-based modeling methods.

Object-Oriented Modeling and Simulation of Optical Burst Switched Mesh Networks

This chapter presents an object-oriented approach for the development of an optical burst switching (OBS) simulator, called OBSim, built in Java. Optical burst switching (OBS) has been proposed to overcome the technical limitations of optical packet switching and optical circuit switching. Due to the high costs of an OBS network infrastructure and a significant number of unanswered questions regarding OBS technology, simulators are a good choice for simulation and estimation of the performance of this kind of networks. OBSim allows the simulation and evaluation of the performance of IP over OBS mesh networks. A detailed description of the design, implementation and validation of this simulation tool is presented.

Virtual Reality and Augmented Reality Applied to Simulation Visualization

This chapter introduces virtual reality and augmented reality as a basis for simulation visualization. It shows how these technologies can support simulation visualization and gives important considerations about the use of simulation in virtual and augmented reality environments. Hardware and software features, as well as user interface and examples related to simulation, using and supporting virtual reality and augmented reality, are discussed, stressing their benefits and disadvantages. The chapter intends to discuss virtual and augmented reality in the context of simulation, emphasizing the visualization of data and behavior of systems. The importance of simulation to give dynamic and realistic behaviors to virtual and augmented reality is also pointed out. The work indicates that understanding the integrated use of virtual reality and simulation should create better conditions to the development of innovative simulation environments as well as to the improvement of virtual and augmented reality environments.

Simulation and Modelling Knowledge-Mining Architectures Using Recurrent Hybrid Nets

Hybrid recurrent nets combine arithmetic and integrator elements to form nodes for modeling the complex behaviour of intelligent systems with dynamics. Given the behaviour pattern of such nodes it is required to determine the values of their causal parameters. The architecture of this knowledge mining process consists of two stages: time derivatives of the trajectory are determined first, followed by the parameters. Hybrid recurrent nets of first order are employed to compute derivatives continuously as the behaviour is monitored. A further layer of arithmetic and hybrid nets is then used to track the values of the causal parameters of the knowledge mining model. Applications to signal processing are used to illustrate the techniques. The theoretical foundations of this knowledge mining process is presented in the first part of the chapter, where the application of dynamical systems theory is extended to abstract systems to illustrate its broad relevance to any system including biological and non physical processes. It models the complexity of systems in terms of observability and controllability.

Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

This chapter reports on the use of simulation in supporting decision-making about what data to collect in a randomized clinical trial (RCT). We show how simulation also allows the identification of critical variables in the RCT by measuring their effects on the simulation model’s “behavior.” Healthcare systems pose many of the challenges, including difficulty in understanding the system being studied, uncertainty over which data to collect, and problems of communication between problem owners. In this chapter we show how simulation also allows the identification of critical variables in the RCT by measuring their effects on the simulation model’s “behavior.” The experience of developing the simulation model leads us to suggest simple but extremely valuable lessons. The first relates to the inclusion of stakeholders in the modeling process and the accessibility of the resulting models. The ownership and confidence felt by stakeholders in our case is, we feel, extremely important and may provide an example to others developing models.

Methodologies and Approaches in Discrete Event Simulation

This chapter aims to give a comprehensive explanatory platform of simulation background. As this chapter comprises of four sections, it reviews simulation definitions, forms of models, the need for simulation, simulation approaches and modeling notations. Simulation definition is essential in order to set research boundaries. Moreover, the chapter discusses forms of models: scale model of the real system, or discrete and continuous models. Subsequently, the chapter states documentation of several reasons by different authors pertaining to the question of “why simulate?,” followed by a thorough discussion of modeling approaches in respect to general considerations. Simulation modeling approaches are discussed with special emphasis on the discrete events type only: process-interaction, event scheduling, and activity scanning, yet, a slight comparison is made between the different approaches. Furthermore, the chapter discusses modeling notations activity cycle diagram (ACD) with different versions of the ACD. Furthermore, the chapter discusses petri nets, which handle concurrent discrete events dynamic systems simulation. In addition, Monte Carlo simulation is discussed due to its important applications. Finally, the fourth section of this chapter reviews Web-based simulation, along with all three different types of object-oriented simulation and modeling.

Emotional Agen Modelig (EMAM)

Artificial emotions play an important role at the control level of agent architectures: emotion may lead to reactive or deliberative behaviors, it may intensify an agent’s motivations, it can create new goals (and then sub-goals) and it can set new criteria for the selection of the methods and the plans the agent uses to satisfy its motives. Since artificial emotion is a process that operates at the control level of agent architecture, the behavior of the agent will improve if agent’s emotion process improves (El-Nasr, Ioerger, & Yen, 1998; El-Nasr & Yen, 1998). In this introductory chapter, our aim is to build agents with the mission "to bring life" several applications, such as: information, transaction, education, tutoring, business, entertainment and e-commerce. Therefore we want to develop artificial mechanisms that can play the role emotion plays in natural life. We call these mechanisms “artificial emotions” (Scheutz, 2004). As Damasio (1994) argues, emotions are necessary for problem solving because when we plan our lives, rather than examining every opinion, some possibilities are emotionally blocked off. We will try to investigate if artificial emotional control can improve performance of the agent in some circumstances. We would like to introduce the readers to our model, which is based on both symbolic and computational relations. Simulations are left for another publication. The space available is barely enough to give an overall picture about our model. The main contributions of this proposal model is to argue that emotion learning is a valid approach to improve the behavior of artificial agents, and to present a systematic view of the kinds of emotion learning that can take place, assuming emotion is a process involving assessment, emotion-signal generation, emotion-response and then emotion learning (LeDoux, 1996). To come across as emotional, an agent needs to incorporate a deeper model of personality, sensitivity, mood, feeling and emotions, and, in particular, directly connect these affective concepts. For agents to be believable, the minds of agents should not be restricted to model reasoning, intelligence and knowledge but also emotions, sensitivity, feeling, mood and personality (Nemani & Allan, 2001). We will propose EMAM (Emotional Agent Model) for this purpose. EMAM generates artificial emotion signals, evaluates and assesses events, takes into account the integration of personality, sensitivity, mood, feeling and motivational states then takes proper action or plans for actions (sequence of actions) (LeDoux, 1996; Gratch, 2000).

Simulating Theory-of Constraint Problem with Novel Fuzzy Compromise Linear Programming Model

This chapter demonstrates development of a novel compromise linear programming having fuzzy resources (CLPFR) model as well as its simulation for a theory-of-constraints’ (TOC) product mix problem using MATLAB® v. 7.04 R.14 SP.2 software. The product-mix problem considers multiple constraint resources. The developed CLPFR model helps in finding a robust solution with better profit and product mix solution in a non-bottleneck situation. The authors simulate the level of satisfaction of the decision maker (DM) as well as the degree of fuzziness of the solution found using the CLPFR model. Simulations have been carried out with MATLAB® v. 7.04 R.14 SP.2 software. In reality, the capacities available for some resources are not always precise. Some tolerances should be allowed on some constraints. This situation reflects the fuzziness in the availability of resources of the TOC product mix problem.

Distributed Simulation in Industry

This chapter introduces the basic concepts of distributed simulation applied to real life industrial cases with particular reference to IEEE 1516 High Level Architecture(HLA): one of de facto standards for distributed simulation. Starting from a concise introduction HLA, the chapter proposes a simple hands-on with a complete implementation example of HLA. Successfully achieved the ability to create small federations, the reader is guided by two real life application of distributed simulation: the first is related to a supply chain modeling for the aerospace industry while the second one is focused on logistic platforms modeling. The authors have edited this chapter keeping in mind usual difficulties that can be encountered in real life projects: for such purpose a reference implementation and full code examples are provided in order to ensure a smooth but effective learning curve. The reader will also find suggestions for proper management of HLA-based simulation projects.