Computationally Efficient Simulation of Queues: The R Package queuecomputer

Large networks of queueing systems model important real-world systems such as MapReduce clusters, web-servers, hospitals, call centers and airport passenger terminals. To model such systems accurately, we must infer queueing parameters from data. Unfortunately, for many queueing networks there is no clear way to proceed with parameter inference from data. Approximate Bayesian computation could offer a straightforward way to infer parameters for such networks if we could simulate data quickly enough. We present a computationally efficient method for simulating from a very general set of queueing networks with the R package queuecomputer. Remarkable speedups of more than 2 orders of magnitude are observed relative to the popular DES packages simmer and simpy. We replicate output from these packages to validate the package. The package is modular and integrates well with the popular R package dplyr. Complex queueing networks with tandem, parallel and fork/join topologies can easily be built with these two packages together. We show how to use this package with two examples: a call center and an airport terminal.Comment: Updated for queuecomputer_0.8.

Modelling wear degradation in cylinder liners

We present and discuss a stochastic model describing the wear process of cylinder liners in a marine diesel engine. The model is based on a stochastic differential equation and Bayesian inference is illustrated. Corrosive action and measurement error, both quite negligible, are modeled with a Wiener process whereas a jump process is used to describe the contribution of soot particles to the wear process. The model can be used to forecast the wear process and, consequently, plan condition based maintenance activities. In the paper, we provide a critical illustration of the mathematical and computational aspects of the model. We propose a strategy that, implemented for simulated and real data, allows for stable parameter estimation and forecasts

A Bayesian approach to improving estimate to complete

The capability to develop a reliable ‘Estimate at Completion’ from the earliest stage of project execution is essential in order to develop a proactive project management. This paper provides a methodology to support the development of the Estimate at Completion in large engineering projects. In order to accomplish this aim, a model to formulate estimates at completion is presented which integrates through a Bayesian approach three knowledge sources: experts’ opinions, data from past projects and the current performance of the ongoing project. The model has been applied to three Oil and Gas projects in order to forecast their final duration and cost. These projects are characterized by a high level of size, uncertainty and complexity representing a challenging test for the model. The results obtained show a higher forecasting accuracy of the Bayesian model compared to the traditional Earned Value Management (EVM) methodology. Moreover, the estimates at completion calculated using the Bayesian model are not point estimates such as those calculated by EVM. In fact, the Bayesian approach leads to a probability density function for the forecasted final cost and duration. Hence, the project manager obtains an indication of the degree of confidence about the expected value forecasted which results in better quality information available for the decision making process

Integrating clinicians’ opinion in the Bayesian meta-analysis of observational studies: the case of risk factors for falls in community-dwelling older people

Background: despite the widespread application of Bayesian methods in meta-analysis, the incorporation of clinical informative priors based upon expert opinion is rare. Methods: a questionnaire to elicit beliefs about five risk factors for falls in older people was administered to a sample of geriatricians and general practitioners (GPs). The experts were asked to provide a point estimate and upper and lower limits of each relative risk. The elicited opinions were translated into different prior distributions and included in a Bayesian meta-analysis of prospective studies. Frequentist, Bayesian non-informative and fully Bayesian approaches were compared. Results: almost all the clinicians provided the requested information. In most cases, the variability across published studies was greater or similar to that across clinicians. Geriatricians provided more consistent estimates than GPs. When fewer studies were available, the use of the informative prior provided by geriatricians reduced the width of the credibility interval with respect to the frequentist or Bayesian non-informative approaches. Enthusiastic and skeptical priors led to results strongly driven by the prior distribution. Conclusions: this study presents a feasible method for belief elicitation and Bayesian priors’ assessment. The inclusion of external information showed to be useful when only few and/or heterogeneous studies were available from the literature

Prior and Posterior Linear Pooling for Combining Expert Opinions: Uses and Impact on Bayesian Networks—The Case of the Wayfinding Model

The use of expert knowledge to quantify a Bayesian Network (BN) is necessary when data is not available. This however raises questions regarding how opinions from multiple experts can be used in a BN. Linear pooling is a popular method for combining probability assessments from multiple experts. In particular, Prior Linear Pooling (PrLP), which pools opinions and then places them into the BN, is a common method. This paper considers this approach and an alternative pooling method, Posterior Linear Pooling (PoLP). The PoLP method constructs a BN for each expert, and then pools the resulting probabilities at the nodes of interest. The advantages and disadvantages of these two methods are identified and compared and the methods are applied to an existing BN, the Wayfinding Bayesian Network Model, to investigate the behavior of different groups of people and how these different methods may be able to capture such differences. The paper focusses on six nodes Human Factors, Environmental Factors, Wayfinding, Communication, Visual Elements of Communication and Navigation Pathway, and three subgroups Gender (Female, Male), Travel Experience (Experienced, Inexperienced), and Travel Purpose (Business, Personal), and finds that different behaviors can indeed be captured by the different methods