Data-driven simulation of networks with manufacturing blocking

ABSTRACT: We develop and empirically test a parsimonious model of how specific knowledge and the value of knowledge sharing influence manufacturing plants' incentive design choices. Our results confirm the prediction that increases in the extent of agents' specific knowledge and the value of knowledge sharing are associated with greater (less) reliance on output (input) performance measures. Moreover, consistent with our model's prediction, we find that plants rely more on group-based (as opposed to individual-based) output performance measures when the value of knowledge sharing is higher, or the extent of agents' specific knowledge is lower. Finally, consistent with previous research, we find that as output performance measures become noisier, firms rely less on these measures in incentive contracts.

Simulation of computer networks is an area of growing interest. Network simulation is a multidisciplinary endeavor requiring expertise in computer networking, modeling and simulation, and software engineering. Network modeling and simulation tools in the research community provide important exemplar technology that has the potential for reuse. NetSim, developed at the Massachusetts Institute of Technology, is an excellent example of a network simulation infrastructure that can support a variety of modifications. In this paper we discuss NetSim and two additional tools: the Maryland Routing Simulator and the Texas A&M Asynchronous Transfer Mode Simulator, which are both modifications of NetSim. NetSim can serve as a simple but effective Ethernet or point-to-point network simulator. Additionally, NetSim can serve as a rapid prototyping tool for other network simulators.

This paper presents an approach, based on ant techniques, to effectively address the assembly line balancing problem with the complicating factors of parallel workstations, stochastic task durations, and mixed-models. A methodology was inspired by the behavior of social insects in an attempt to distribute tasks among workers so that strategic performance measures are optimized. This methodology is used to address several assembly line balancing problems from the literature. The assembly line layouts obtained from these solutions are used for simulated production runs so that output performance measures (such as cycle time performance) are obtained. Output performance measures resulting from this approach are compared to output performance measures obtained from several other heuristics, such as simulated annealing. A comparison shows that the ant approach is competitive with the other heuristic methods in terms of these performance measures.

Sensitivity analysis for output performance measures in long-range dependent queueing system

Introduction Robust scheduling is one of the approaches adopted to deal with uncertainties in stochastic scheduling. Since these uncertainties, arising as they do from variability in the scheduling parameters, significantly affect the performance of a system, a viable approach has been to determine a robust schedule that is least susceptible to disturbances or variations in the input parameters and that has minimum variability in its output performance measure. For instance, when processing times are uncertain, a robust schedule would have the least variance of the output performance measure, although it might not necessarily have the least expected value. As also mentioned in Chapter 1, of the various parameters involved in scheduling, we intend to focus on the uncertainty factor associated with job processing times. The following sections detail different robust scheduling methodologies that are available to model certain scheduling environments and the underlying principles behind those various approaches. Our primary focus is to elicit and delve in detail on the modeling approaches and not to expostulate the solution methodologies. However, we do briefly discuss the various solution approaches that are used to solve these robust scheduling problems and also state relevant results (as propositions) that are used in developing these solution methodologies. We intend to capture the core techniques that have been used in modeling robustness in scheduling and would advise interested readers to refer to the appropriate literature for a detailed and complete exposition.

The time required to execute simulation models of modern production systems remains high even with todays computing power, particularly when what-if analyses need to be performed to investigate the impact of controllable system input variables on an output performance measure. Compared to mean and variance which are frequently used in practice, quantiles provide a more complete picture of the performance of the underlying system. Nevertheless, quantiles are more difficult to estimate efficiently through stochastic simulation. Stochastic kriging (SK) and quantile regression (QR) are two promising metamodeling tools for addressing this challenge. Both approximate the functional relationship between the quantile parameter of a random output (e.g., cycle time) and multiple input variables (e.g., start rate, unloading times). In this paper, we compare performances of SK and QR on steady-state quantile parameter estimation. Results are presented from simulations of an M/M/1 queue and a more realistic model of a semiconductor manufacturing system.

Size optimization of a PV/wind hybrid energy conversion system with battery storage using response surface methodology

This chapter introduces simulation tool TRANSSIM (Transportation model Simulation) to simulate transportation models. TRANSSIM is a tool which simulates and compares the results of different transportation models. A combination of programming languages is used to design this tool and is based on analytical approach to guide optimization strategy. In TRANSSIM, inputs are provided in terms of resources available, requirement and cost associated. Output performance measurements are calculated in terms of product allocation and associated total cost.

The study was conducted with a view to evaluate the performance of the University of Malawi’s Polytechnic using the Balanced Scorecard model. The research employed a deductive approach and adopted a survey strategy.  A closed questionnaire eliciting demographic characteristics and work related factors was sent to a sample of the Polytechnic stakeholders who were selected using a non-random purposive technique.  The results indicated that the institution’s performance was poor on all the perspectives of the balanced scorecard. However, the performance of the college based on output performance measures was rated as good. The results have also shown that the internal processes and innovation, and learning processes showed a strong correlation to performance measures.   Key words: Balanced scorecard, financial perspective, customer perspective, internal processes, innovation and learning, performance measures.

A certain degree of machining must be done on hybrid composites (glass and carbon fibres), created by basic manufacturing procedures, so as to achieve close fits and tolerances. End milling is a common method for finishing machined flat plate hybrid composite products, having aerospace, automobile, wind turbine blade and prosthetic applications. This study was performed using, PVD TiAlN coated carbide tools, under dry conditions. Three input parameters, namely nose radius, having two levels, cutting speed and feed rate, having three levels each were considered. Surface roughness parameter Ra was considered as the output performance measure. Taguchi L18 orthogonal array (OA) was applied for the design of experiments. Response surface methodology was adopted to optimize the input performance parameters. A response surface was generated to investigate the effect of input variables and their interactions on the response. From the analysis of the mean effect plot, it was observed that the most significant input parameter on surface roughness was the feed rate. Optimization results revealed that the best surface roughness was observed at a combination of A1B2C1, i.e. nose radius of 1 mm, cutting speed of 94.69 m/min and feed rate of 300 mm/min.KeywordsCarbon fibreHybrid compositeCFRPResponse surface methodologySurface roughness

We are announcing the first release of Viptos (Visual Ptolemy and TinyOS), an integrated graphical development and simulation environment for TinyOS-based wireless sensor networks. Viptos allows developers to create block and arrow diagrams to construct TinyOS programs from any standard library of nesC/TinyOS components. The tool automatically transforms the diagram into a nesC program that can be compiled and downloaded from within the graphical environment onto any TinyOS-supported target hardware. In particular, Viptos includes the full capabilities of VisualSense [1], which can model communication channels, networks, and non-TinyOS nodes. This release of Viptos is compatible with nesC 1.2 and includes tools to harvest existing TinyOS components and applications and convert them into a format that can be displayed as block (and arrow) diagrams and simulated.Viptos is based on TOSSIM and Ptolemy II. TOSSIM is an interrupt-level simulator for TinyOS programs. It runs actual TinyOS code but provides software replacements for the simulated hardware and models network interaction at the bit or packet level. Ptolemy II is a graphical software system for modeling, simulation, and design of concurrent, real-time, embedded systems. Ptolemy II focuses on assembly of concurrent components with well-defined models of computation that govern the interaction between components. VisualSense is a Ptolemy II environment for modeling and simulation of wireless sensor networks at the network level.Viptos provides a bridge between VisualSense and TOSSIM by providing interrupt-level simulation of actual TinyOS programs, with packet-level simulation of the network, while allowing the developer to use other models of computation available in Ptolemy II for modeling various parts of the system. While TOSSIM only allows simulation of homogeneous networks where each node runs the same program, Viptos supports simulation of heterogeneous networks where each node may run a different program. Viptos simulations may also include non-TinyOS-based wireless nodes. The developer can easily switch to different channel models and change other parts of the simulated environment, such as creating models to generate simulated traffic on the wireless network.Viptos inherits the actor-oriented modeling environment of Ptolemy II, which allows the developer to use different models of computation at each level of simulation. At the lowest level, Viptos uses the discrete-event scheduler of TOSSIM to model the interaction between the CPU and TinyOS code that runs on it. At the next highest level, Viptos uses the discrete-event scheduler of Ptolemy II to model interaction with mote hardware, such as the radio and sensors. This level is then embedded within VisualSense to allow modeling of the wireless channels to simulate packet loss, corruption, delay, etc. The user can also model and simulate other aspects of the physical environment including those detected by the sensors (e.g., light, temperature, etc.), terrain, etc.At IPSN in April 2005, we demonstrated a pre-release developmental version of Viptos with two simple applications. The first was a single node sensing application that displayed the value of the light sensor on the LEDs. The second was a two node send and receive application that transmitted the value of the light sensor on the first node to the second node. This release version of Viptos supports more sophisticated applications, such as multi-node routing, and demonstrates some of the more advanced features described in this abstract.

A multi-purpose 3D simulator named HNMSim (hybrid networked multi-agent system (MAS) simulator) with application in cooperative control is presented in this paper. Based on USARSim (Unified system for automation and robot simulation) [37], Unreal Engine [33], LabView [17], Matlab [20] and OMNet++ [22], this simulator creates a high-fidelity simulation environment for networked MAS. To provide assistant in research and education, we present three interfaces based on LabView, Matlab and OMNet++, respectively. The system structure (hardware-in-the-loop simulation) and design methodology are also briefly described. In addition, the network simulation is highlighted. Furthermore, we show its wide applications in networked MAS by demonstrating its usage in distributed formation control, distributed coverage control and search problem using multiple UAVs/UGVs.

There has been a tremendous increase in the use of wire/wireless Local Area Networks (LAN) with different network configurations. Therefore, it is vital to have an accurate and a reliable generic platform to enable network developers, managers, security mangers, researchers, and students evaluating and investigating the performance of LANs of different technologies and configurations. Many network simulators have been developed throughout the years, such as: the Optimized Network Engineering Tool (OPNET), the Network Simulator (NS), the Global Mobile Simulator (GloMoSim), etc. One of the most widely-used and powerful general-purpose network simulators is OPNET, which is an object-oriented simulation environment. This chapter provides two walk through tutorials on using OPNET IT Guru (Academic Edition of OPNET) for wire/wireless LANs simulations. These tutorials demonstrate in step-by-step fashion, the procedures of initiating new simulation, setting up the simulation parameters, running the simulation, and viewing the results. The first tutorial (Tutorial #1) simulates a wired LAN of 10 computers and one server connected to a single switch, and the second tutorial (Tutorial #2) simulates a wireless ad hoc network of 10 mobile nodes and one server. This chapter demonstrates that OPNET IT Guru is a powerful tool that can be used by a range of professional users to simulate, evaluate, and investigate the performance of wire and wireless LANs. Moreover, it encourages and helps students to easily perform network simulation for better understanding of the network performance under different network conditions.

Exercise Testing and Training Strategies for Healthy and Frail Elderly

Computer networking is taught for some time at universities. A network is a complex mix of applications, communications protocols and link technologies, traffic flows and routing algorithms. Teaching the networking concept, the network design process is a challenging task, requiring designers to balance user performance expectations with costs and capacities. One of the obvious approaches employed to deal with its complexity is through the use of modelling and simulation techniques. This paper examines three best educational tools that can be used in the Computer Networking course at Cardiff Metropolitan University or any University for educational purpose. The paper demonstrates that a successful teaching programme needs to combine a range of teaching tools to achieve its aims. Analysing various aspects of the course provides evaluation of the overall success of the tools. Many network design and simulation tools have been studied. Based on our research and experiment, the best educational tools were selected that could be used for the purpose of teaching in the area of computer communications networks. The wide area network modelling tool Delite, the network simulator ns-3, the topology generation tool Brite can be used together to teach students the networking concepts. These tools can not only help students to understand computer network principles but also improve their network practical skills. Currently there are various tools available that are created by different companies and groups of researchers in academia and that are intended for use as practical and/or educational tools for network design including security issues. To make this task easy, different users, researchers and companies have developed different network modelling and simulation (MS) tools. Very often, such tools are addressing different issues in the network design process which are complementary to each other. It would be useful for learning and improving the network design process by allowing the use of a variety of tools.