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In this paper we discuss the results of extensive research on supply chain modelling and analysis in high-tech supply chains. We distinguish between the forecast-driven and the customer-order-driven part of the supply chain. For each part we present a generic model that captures real-life complexity of high-tech supply chains. With each model comes a class of operational control policies that coordinate...
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 their management. The implementations of these generic ideas in AutoMod, SLX, ExtendSim, and Simio are described. The paper concludes...
In the analysis of input and output models used in computer simulation, parametric bootstrapping provides an attractive alternative to asymptotic theory for constructing confidence intervals for unknown parameter values and functions involving such parameter values, and also for calculating critical values of EDF statistics used in goodness-of-fit tests, such as the Anderson-Darling A2 statistic....
We provide a review of the state of the art on the design and implementation of random number generators (RNGs) for simulation, on both sequential and parallel computing environments. We focus on the need for multiple independent streams and substreams of random numbers, explain how they can be constructed and managed, review software libraries that offer them, and illustrate their usefulness via...
Parallel and distributed simulation is a field concerned with the execution of a simulation program on computing platforms containing multiple processors. This article focuses on the concurrent execution of discrete event simulation programs. The field has evolved and grown from its origins in the 1970's and 1980's and remains an active field of research to this day. An overview of parallel and distributed...
In this tutorial we discuss the use of a recently published statistical procedure for the validation of models that have their required model accuracy specified as a range, often called the acceptable range of accuracy. This new statistical procedure uses a hypothesis test of an interval, considers both Type I and Type II errors through the use of the operating characteristic curve, and provides the...
Embedded systems development has interesting challenges due to the complexity of the tasks they execute. Most of the methods used for developing embedded applications are either hard to scale up for large systems, or require a difficult testing effort with no guarantee for bug-free software products. Instead, construction of system models and their analysis through simulation reduces both end costs...
This tutorial reviews the role of Stochastic Petri Nets (SPNs) in stochastic simulation. The evolution of SPNs as a component-level state-space modeling framework is discussed. SPNs are compared to both process-based approaches to discrete event simulation (DES) and to agent-based modeling (ABM). The causes for the apparent lack of commercial success of general-purpose simulation with SPNs are analyzed...
For large-scale urban system simulations the computing power of traditional workstations is not sufficient. The move to High Performance Computing clusters is a viable solution. Users of such simulations are domain experts with little knowledge in computer science and optimization of such simulations. The access to HPC resources is also not available. Vendors have not sufficiently addressed this....
One of the major trends in traffic simulations is to take into account microscopic aspects of traffic flows at the street level. Multi-agent models such as MATSim (multi-agent transport simulation) have been highlighted for recent years as a solution to address these complex and microscopic simulation requirements. They are viewed as an emergent and collective behavior of agents, (i.e., vehicles)...
This paper proposes a newevolutionary algorithm-based methodology for optimal crowdevacuation planning. In the proposed methodology, a heuristic-based evacuation scheme is firstly introduced. The key idea is to divide the region into a set of sub-regions and use a heuristic rule to dynamically recommend an exit to agents in each sub-region. Then, an evolutionary framework based on the Cartesian Genetic...
Crowd simulation is a well-studied topic, yet it usually focuses on visualization. In this paper, we study a special class of crowd simulation, where individual agents have diverse backgrounds, ad hoc objectives, and non-repeating visits. Such crowd simulation is particularly useful when modeling human agents movement in leisure settings such as visiting museums or theme parks. In these settings,...
We propose a newstochastic model of infectious disease propagation. This model tracks individual outcomes, but does so without needing to create connectivity graphs for all members of the population. This makes the model scalable to much larger populations than traditional agent-based models have been able to cope with, while preserving the impact of variability during the critical early stages of...
Dengue is a febrile disease whose main vector transmitter is the Aedes Aegypti mosquito. This disease has an annual register of 50 million infections worldwide. Simulations are an important tool in helping to combat and prevent the epidemic and, consequently, save lives and resources. Therefore, in this paper, we propose an Agent-Based Model for assessment of the pupal productivity of the Aedes Aegypti...
Emergency departments are currently facing major pressures due to rising demand caused by population growth, aging and high expectations of service quality. With changes continuing to challenge healthcare systems, developing solutions and formulating policies require a good understanding of the complex and dynamic nature of the relevant systems. However, as a typically complex system, it is hard to...
Agent-based simulation (ABS) continues to grow in popularity and in its fast-expanding application in various fields. Despite the increased interest, however, a common protocol or standard curriculum for development and analysis of ABS models hardly exists. As originally discrete-event simulation (DES) modelers, self-taught and still new to the world of ABS modeling, we have occasionally observed...
Agent-based Modeling and Simulation has become a mainstream tool for use in business and research in multiple disciplines. Along with its mainstream status, ABMS has attracted the attention of practitioners who are not always comfortable developing software in Java, C++ or any of the scripting languages commonly used for ABMS frameworks. In particular, animal behavior researchers, or ethologists,...
Agent-based models (ABMs) are ubiquitous in research and industry. Currently, simulating ABMs involves at least some imperative (step-by-step) computer instructions. An alternative approach is declarative programming, in which a set of requirements is described at a high level of abstraction. Here I present the a fully declarative methodology for the automated construction of simulations for ABMs...
Supply chain collaboration is considered to be the main driving force of supply chain success. In practice, however, ideal supply chain collaboration is difficult to achieve. In particular, a factor that is presumed to hinder collaboration is competition between firms. Even though several studies suggest that competition benefits supply chains, other studies come to the opposite conclusion. In order...
To achieve competitive advantage, companies have been driven to improve their supply chain by outsourcing their non-core business. However, this increases the external risks, such as the demand and supply risks. Companies face challenges in defining effective supply chain topology to mitigate supply chain risks. In this research, we design supply chain network topologies to mitigate the demand and...
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