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The continued popularity of social media in the dissemination of ideas and the unique features of that channel create important research opportunities in the study of rumor contagion. Using an agent-based modeling framework, we study agent behavior in the spread of competing rumors through an endogenous costly exercise of measured networked interactions whereby agents update their position, opinion...
This paper presents an integrated approach for enhancing the performance of stochastic optimization processes by incorporating techniques from statistical experimental designs, such as response surface methodology. The two-stage process includes an “exploratory” phase, during which a fraction of the finite time budget is reserved for conducting informative measurements to best approximate the stochastic...
System dynamics, which is an approach built on information feedbacks and delays in the model in order to understand the dynamical behavior of a system, has successfully been implemented for supply chain management problems for many years. However, research within in multi-objective optimization of supply chain problems modelled through system dynamics has been scares. Supply chain decision making...
Reliable simulation estimation builds on accurately specified input models. In the context of simulating tail events, knowledge on the tail of the input model is especially important, yet is often hard to obtain due to a lack of data. In this paper, we consider tail event estimation without any knowledge on the input tail, but rather only making a general assumption that it is convex. We focus on...
In pre-hospital health care the call center plays an important role in the coordination of emergency medical services (EMS). An EMS call center handles inbound requests for EMS and dispatches an ambulance if necessary. The time needed for triage and dispatch is part of the total response time to the request, which, in turn, is an indicator for the quality of EMS. Calls entering an efficient EMS call...
Stochastic optimization facilitates decision making in uncertain environments. In typical problems, probability distributions are fit to historical data for the chance variables and then optimization is carried out, as if the estimated probability distributions are the “truth”. However, this perspective is optimistic in nature and can frequently lead to sub-optimal or infeasible results because the...
In this paper, we consider the problem of efficiently identifying the Pareto optimal designs out of a given set of alternatives, for the case where alternatives are evaluated on multiple stochastic criteria, and the performance of an alternative can only be estimated via sampling. We propose a simple myopic budget allocation algorithm based on the idea of small-sample procedures. Initial empirical...
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...
In stochastic simulation, we construct mathematical models to imitate the behavior of real systems, use computers to sample behavioral histories (sample paths) of these models, and exploit those samples to improve decision making with the real system. The imitation part can be very challenging, in particular for modeling uncertainty. Fitting univariate probability distribution to data is far from...
Combat modeling is a key area of military science and related research. Here, we propose a moment matching scheme with a modified stochastic Lanchester-type model. An experiment shows that the proposed scheme makes approximations more rapidly while maintaining a high level of accuracy compare to the Markovian model.
We consider a chance-constrained two-stage stochastic scheduling problem for multi-skill call centers with uncertainty on arrival rate and absenteeism. We first determine an initial schedule based on an imperfect forecast on arrival rate and absenteeism. Then, this schedule is corrected applying recourse actions when the forecast becomes more accurate in order to satisfy the service levels and average...
In this paper simulation modeling of a brewery bottling line is described. Reference nets as an extended version of high level Petri nets are being used for the modeling environment and make use of external Java programming language based models. The study focuses on a bottling line used within a small-to-medium sized brewery. Machine data, flow measurements and the determination of the chemical oxygen...
When simulating a complex stochastic system, the behavior of the output response depends on the input parameters estimated from finite real-world data, and the finiteness of data brings input uncertainty to the output response. The quantification of the impact of input uncertainty on output response has been extensively studied. However, most of the existing literature focuses on providing inferences...
Motivated by our recent extension of the Two-Stage Sequential Algorithm (eTSSO), we propose an adaptation of the framework in Pasupathy et al. (2015) for the study of convergence of kriging-based procedures. Specifically, we extend the proof scheme in Pasupathy et al. (2015) to the class of kriging-based simulation-optimization algorithms. In particular, the asymptotic convergence and the convergence...
This paper investigates empirically two-range robust optimization (2R-RO) as an alternative to stochastic programming in terms of computational time and solution quality. We consider a number of possible projects with anticipated costs and cash flows, and an investment decision to be made under budget limitations. In 2R-RO, each uncertain parameter is allowed to take values from more than one uncertainty...
Stochastic kriging (SK) has been recognized as a useful and effective technique for approximating the response surface of a simulation model. In this paper, we analyze the performance of SK metamodels in a fully sequential setting when design points are selected one at a time. We consider both cases when the trend term in the model is either known or estimated and show that the prediction performance...
This paper identifies construction constraints for a constraint simulation of a construction flow. Therefore the construction environment and the methodologies of scheduling in construction are analyzed. Typical characteristics of construction schedules are classified. The relationship between different activities or between activities and building elements or between different building elements are...
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...
A simulation model that supports the planning of an offshore emergency response system is presented. This model is based on official guidelines for offshore preparedness and can be used to evaluate different designs of an emergency system in respect to quantity, performance and location of Search-and-Rescue helicopters by modeling the coverage of the area under consideration. The model is trace-driven...
In this paper, we introduce an intelligent vehicle in traffic flow where a phantom traffic jam occurs for ensuring traffic-flow stability. The intelligent vehicle shares information on the speed and gap of the leading vehicle. Furthermore, the intelligent vehicle can foresee changes in the leading vehicles through shared information and can start accelerating faster than human-driven vehicles can...
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