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At The Boeing Company, stock levels for maintenance spares with substantial lead times must be established before fielding new aircraft designs. Initial calculations use mean time between demand estimates developed by the engineering department. After sufficient operating hours, stock levels are recalculated using statistical forecasts of maintenance history. A Bayesian forecasting method was developed...
Simulation is often used to study stochastic systems. A key step of this approach is to specify a distribution for the random input. This is called input modeling, which is important and even critical for simulation study. However, specifying a distribution precisely is usually difficult and even impossible in practice. This issue is called input uncertainty in simulation study. In this paper we study...
We describe basic research that uses a causal, uncertainty-sensitive computational model rooted in qualitative social science to fuse disparate pieces of threat information. It is a cognitive model going beyond rational-actor methods. Having such a model has proven useful when information is uncertain, fragmentary, indirect, soft, conflicting, and even deceptive. Inferences from fusion must then account...
We consider linear programs where some parameters in the objective functions are unknown but data are available. For a risk-averse modeler, the solutions of these linear programs should be picked in a way that can perform well for a range of likely scenarios inferred from the data. The conventional approach uses robust optimization. Taking the optimality gap as our loss criterion, we argue that this...
Stochastic simulation is driven by the input model, which is a collection of distributions that model the randomness in the system. The input model is often constructed from data, and hence input uncertainty arises due to the finiteness of data. Simulation optimization has been mostly studied under the assumption of a known input model, without accounting for input uncertainty. We propose a new framework...
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...
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...
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...
The Bourgoyne and Young Model (BYM) is used to determine the rate of penetration in oil well drilling processes. To achieve this the model must be parameterized with coefficients that are estimated on the basis of prior experience. Since drilling is a physical process, measurement data may include noise and the model may naturally fail to represent it correctly. In this study the BYM coefficients...
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...
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...
Simulation used for the performance assessment of stochastic systems is usually driven by input models estimated from real-world data, which introduces both input and simulation uncertainty to the performance estimates. For many complex systems, because the components of input models are mutually dependent, an efficient estimation of dependence could improve the system performance assessment. Since...
When we use simulations to estimate the performance of a stochastic system, simulations are often driven by input distributions that are estimated from real-world data. There is both input and simulation uncertainty in the performance estimates. Non-parametric sampling approaches, e.g., the bootstrap, could be used to generate samples of input distributions quantifying both input model and parameter...
The indifference-zone (IZ) formulation of ranking and selection (R&S) is the foundation of many procedures that have been useful for choosing the best among a finite number of simulated alternatives. Of course, simulation models are imperfect representations of reality, which means that a simulation-based decision, such as choosing the best alternative, is subject to model risk. In this paper...
We study a subset selection procedure - one of the well-known statistical methods of ranking and selection for stochastic simulations - in the presence of input parameter uncertainty; i.e., the parameters of the input distributions are unknown and there is only a limited amount of input data available for input parameter estimation. The goal is to present a new decision rule which identifies subsets...
Performance analysis via stochastic simulation is often subject to input model uncertainty, meaning that the input model is unknown and needs to be inferred from data. Motivated especially from situations with limited data, we consider a worst-case analysis to handle input uncertainty by representing the partially available input information as constraints and solving a worst-case optimization problem...
We consider stochastic optimization problems in which the input probability distribution is not fully known, and can only be observed through data. Common procedures handle such problems by optimizing an empirical counterpart, namely via using an empirical distribution of the input. The optimal solutions obtained through such procedures are hence subject to uncertainty of the data. In this paper,...
Clinical laboratory measurements are vital to the medical decision-making process, and specifically, measurement of rheumatoid factor antibodies is part of the disease criteria for various autoimmune conditions. Uncertainty estimates describe the quality of the measurement process, and uncertainty in calibration of the instrument used in the measurement can be an important contributor to the net measurement...
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