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Minimum price is not the only objective that companies pursue when sourcing their materials. Selecting the best supplier entails looking for the best quality as well as the most reliable delivery. This work suggests a Multi-Criteria objective function that linearly aggregates a number of Taguchi loss functions, which represent the criteria of price, quality, and delivery. We initially recommend a...
Mathematical programming representation has been recently used to describe the behavior of discrete event systems as well as their formal properties. This paper proposes approximate mathematical programming models for the simulation-optimization of flow lines with finite buffer capacities. The approximation exploits the concept of time buffer, modeled as a constraint that put into a temporal relationship...
This paper reports on an empirical study that explores the model development process followed by six expert modelers in discrete-event simulation (DES). So far the model development practice in DES has not been explored and there is little understanding of the processes followed. This study observes the modeling process of practitioners, experts in simulation modeling undertaking a laboratory modeling...
Our main aim in this paper is to highlight current practice and education in multiple scenario comparison within DES experimentation and to illustrate the possible benefits of employing false discovery rate (FDR) control as opposed to strict family-wise error rate (FWER) control when comparing large numbers of scenarios in an exploratory manner. We present the results of a small survey into the current...
We derive Monte Carlo simulation estimators to compute option price sensitivities under the SABR stochastic volatility model. As a companion to the exact simulation method developed by Cai, Chen and Song (2011), this paper uses the sensitivity of “vol of vol” as a showcase to demonstrate how to use the pathwise method to obtain unbiased estimators to the price sensitivities under SABR. By appropriately...
The paucity of existing employer-driven simulation education and the need for workers broadly trained in Modeling & Simulation (M&S) poses a critical need that the simulation community as a whole must address. This paper will describe how this need became an impetus for a new inter-university activity that allows students to learn about simulation by doing it. The event, called Smackdown,...
The use of Surrogate Based Optimization (SBO) has become commonplace for optimizing expensive black-box simulation codes. A popular SBO method is the Efficient Global Optimization (EGO) approach. However, the performance of SBO methods critically depends on the quality of the guiding surrogate. In EGO the surrogate type is usually fixed to Kriging even though this may not be optimal for all problems...
We consider the problem of efficient simulation estimation of the density function at the tails, and the probability of large deviations for an average of independent, identically distributed light-tailed random variables. The latter problem has been extensively studied in literature where state independent exponential twisting based importance sampling has been shown to be asymptotically efficient...
We study the problem of estimating small failure probabilities for elastic random material described by a one dimensional stochastic elliptic differential equation with certain external forcing and boundary conditions. Gaussian random functions are used to model the spatial variation of the material parameters. The failure event of the bulk material is simply characterized by the exceeding of certain...
Importance sampling in the setting of heavy tailed random variables has generally focused on models with additive noise terms. In this work we extend this concept by considering importance sampling for the estimation of rare events in Markov chains of the form equation where the Bn's and An's are independent sequences of independent and identically distributed (i.i.d.) random variables and the Bn's...
Monte Carlo simulation has been widely used as a standard tool for estimating expectations. In this paper we develop a variance reduction technique for a particular case when the expectation is taken under a constraint that a sum of random variables is larger than a threshold. The proposed technique is based on a reflection argument on the sample space and requires knowing the joint density of the...
Management policies for disease outbreaks balance the expected morbidity and mortality costs versus the cost of intervention policies. We present a methodology for dynamic determination of optimal policies in a stochastic compartmental model with parameter uncertainty. Our approach is to first carry out sequential Bayesian estimation of outbreak parameters and then solve the dynamic programming equations...
Multiple exercise options generalize American-style options as they allow the holder multiple exercise rights and control over the exercise amounts. They arise in both real and financial option applications, such as tolling agreements and swing options which are primarily used in the energy industry. The Forest of Stochastic Meshes is a recently proposed simulation method for valuing such options...
We explore a bottom-up approach to revisit the problem of cash flow modeling in insurance business, and propose a methodology to efficiently simulate the related tail quantities, namely the fixed-time and the finite-horizon ruin probabilities. Our model builds upon the micro-level contract structure issued by the insurer, and aims to capture the bankruptcy risk exhibited by the aggregation of policyholders...
Stochastic volatility models capture the impact of time-varying volatility on the financial markets, and hence are heavily used in financial engineering. However, stochastic volatility is not directly observable in reality, but is only “partially” observable through the inference from the observed asset price. Most of the past research studied American option pricing in stochastic volatility models...
The paper develops valuation of multi-name credit derivatives, such as collateralized debt obligations (CDOs), based on a novel multivariate subordinator model of dependent default (failure) times. The model can account for high degree of dependence among defaults of multiple firms in a credit portfolio and, in particular, exhibits positive probabilities of simultaneous defaults of multiple firms...
We consider a model of an irreducible network in which each node is subjected to a random demand, where the demands are jointly normally distributed. Each node has a given supply that it uses to try to meet its demand; if it cannot, the node distributes its unserved demand equally to its neighbors, which in turn do the same. The equilibrium is determined by solving a linear program (LP) to minimize...
Modeling is an essential part of construction project planning and control. Most modeling exercises use the Critical Path Method (CPM) since it is simple to use and versatile, despite its lack of versatility. Almost all other modeling techniques are aimed at specialized types of construction work, such as linear scheduling which is used for modeling work that progresses along a line. Discrete-event...
Building Information Modeling (BIM)-based simulation models have been used to automate lengthy building energy modeling processes and it enable fast acquisition of results. Recent improvements of simulation programs have continued to the increase in the use of energy simulation in sustainability studies at the earlier design stage. However, it is often difficult to leverage the full potential of BIM...
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