'Some tactical problems in digital simulation' for the next 10 years

In his influential 1963 paper ‘Some Tactical Problems in Digital Simulation’, Conway identified important issues that became the pillars of research in simulation analysis methodology. Naturally these ‘problems’ were a product of the applications of interest at the time, as well as the state of simulation and computing, much of which has changed dramatically. In light of those changes, we attempt to identify the tactical problems that might occupy simulation researchers for the next 10 years

From Data to Stochastic Modeling and Decision Making:What Can We Do Better?

In the past decades we have witnessed a paradigm-shift from scarcity of data to abundance of data. Big data and data analytics have fundamentally reshaped many areas including operations research. In this paper, we discuss how to integrate data with the model-based analysis in a controlled way. Specifically, we consider techniques to quantify input uncertainty and the decision making under input uncertainty. Numerical experiments demonstrate that different ways in decision making may lead to significantly different outcomes in a maintenance problem

A Two Stage Algorithm for Guiding Data Collection Towards Minimising Input Uncertainty

In stochastic simulation the input models used to drive the simulation are often estimated by collecting data from the real-world system. This can be an expensive and time consuming process so it would therefore be useful to have some guidance on how much data to collect for each input model. Estimating the input models via data introduces a source of variance in the simulation response known as input uncertainty. In this paper we propose a two stage algorithm that guides the initial data collection procedure for a simulation experiment that has a fixed data collection budget, with the objective of minimising input uncertainty in the simulation response. Results show that the algorithm is able to allocate data in a close to optimal manner and compared to two alternative data collection approaches returns a reduced level of input uncertainty

Comparing Data Collection Strategies via Input Uncertainty When Simulating Testing Policies Using Viral Load Profiles

Temporal profiles of viral load have individual variability and are used to determine whether individuals are infected based on some limit of detection. Modelling and simulating viral load profiles allows for the performance of testing policies to be estimated, however viral load behaviour can be very uncertain. We describe an approach for studying the input uncertainty passed to simulated policy performance when viral load profiles are estimated from different data collection strategies. Our example shows that comparing the strategies solely based on input uncertainty is inappropriate due to the differences in confidence interval coverage caused by negatively biased simulation outputs