Discrete-event simulation models generate random variates from input distributions and compute outputs according to the simulation logic. The input distributions are typically fitted to finite real-world data and thus are subject to estimation errors that can propagate to the simulation outputs: an issue commonly known as input uncertainty (IU). This paper investigates quantifying IU using the output confidence intervals (CIs) computed from bootstrap quantile estimators. The standard direct bootstrap method has overcoverage due to convolution of the simulation error and IU; however, the brute-force way of washing away the former is computationally demanding. We present two new bootstrap methods to enhance direct resampling in both statistical and computational efficiencies using shrinkage strategies to down-scale the variabilities encapsulated in the CIs. Our asymptotic analysis shows how both approaches produce tight CIs accounting for IU under limited input data and simulation effort along with the simulation sample-size requirements relative to the input data size. We demonstrate performances of the shrinkage strategies with several numerical experiments and investigate the conditions under which each method performs well. We also show advantages of nonparametric approaches over parametric bootstrap when the distribution family is misspecified and over metamodel approaches when the dimension of the distribution parameters is high. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Science Foundation [CAREER CMMI-1834710, CAREER CMMI-2045400, DMS-1854659, and IIS-1849280]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0044 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0044 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Input model bias is the bias found in the output performance measures of a simulation model caused by estimating the input distributions/processes used to drive it. When the simulation response is a nonlinear function of its inputs, as is usually the case when simulating complex systems, input modelling bias is amongst the errors that arise. In this paper, we introduce a method that recalibrates the input parameters of parametric input models to reduce the bias in the simulation output. The proposed method is based on sequential quadratic programming with a closed form analytical solution at each step. An algorithm with guidance on how to practically implement the method is presented. The method is shown to be successful in reducing input modelling bias and the total mean squared error caused by input modelling error. Summary of Contribution: This paper furthers the understanding and treatment of input modelling error in computer simulation. We provide a novel method for reducing input model bias by recalibrating the input parameters used to drive a simulation model. A sequential quadratic programming approach with an explicit solution is provided to recalibrate the input parameters. The method is therefore computationally inexpensive. An algorithm outlining our proposed procedure is provided within the paper. An evaluation of the method shows the method successfully reduces input model bias and may also reduce the mean squared error caused by input modelling in the output of a simulation model.

We consider the problem of deriving confidence intervals for the mean response of a system that is represented by a stochastic simulation whose parametric input models have been estimated from “real-world” data. As opposed to standard simulation confidence intervals, we provide confidence intervals that account for uncertainty about the input model parameters; our method is appropriate when enough simulation effort can be expended to make simulation-estimation error relatively small. To achieve this we introduce metamodel-assisted bootstrapping that propagates input variability through to the simulation response via an equation-based model rather than by simulating. We develop a metamodel strategy and associated experiment design method that avoid the need for low-order approximation to the response and that minimizes the impact of intrinsic (simulation) error on confidence level accuracy. Asymptotic analysis and empirical tests over a wide range of simulation effort show that confidence intervals obtained via metamodel-assisted bootstrapping achieve the desired coverage.

The design of large and complex enterprise integration solutions is a difficult task. It can require solutions that are unique because of constraints from the current set of legacy applications. Design knowledge for enterprise integration solutions is, therefore, difficult to articulate and reuse. In particular, the nature and form of knowledge for conceptual design of integration solutions is difficult to pin down. In this paper, we investigate whether design knowledge for enterprise integration in the form of patterns can be reused to develop systems integration solutions, and whether such reuse leads to more effective design outcomes. The research follows design science guidelines in which we describe a research artifact, and evaluate it to assess whether it meets the intended goals. The results indicate that approaches to facilitate reuse of conceptual design knowledge are feasible in the domain of enterprise integration, and that such reuse does, in fact, lead to more effective design solutions.

To predict the delay between a source and a destination as well as to identify anomalies in a network, it is possible to monitor the network continuously by sending probes between all sources and destinations. However, it is of prime importance to keep the number of probes to a minimum and yet be able to predict the delays and identify anomalies reasonably. We state and solve a mathematical programming problem, namely the zone recovery methodology (ZRM), to select an optimal subset of ping-like probes to monitor networks where the topology and routing information are not known. A polynomial-time heuristic is developed. The application of ZRM on randomly generated topologies yielded 73.55% reduction in the number of monitored paths on average. In other words, networks can be successfully monitored using only 26.45% of the available probes. Moreover, the performance of ZRM increases (percentage of the monitored paths decreases) as the size of the topology increases.