Papers & Publications

Simulations are a good way for project managers to assess the impact of uncertainties on project plans and subsequent execution. Three types of simulations have been used in literature and to a lesser extent in practice for such purpose, Monte Carlo, discrete event, and systems dynamics. It behooves the project managers to understand the applicability of these different simulations and associated advantages and disadvantages. The paper will help analysts focused on single type of simulation in the past to appreciate the capabilities of alternate approaches. It will also help practicing project managers to appreciate the effort involved, the analysis generated from the three simulations, and factors to determine which one to employ based on their objectives

Resource overload is a primary cause of bad multitasking and delay in projects. It is very difficult to determine the workload expected to be done by any particular resource at any moment in time. But a simple algorithm can provide a "good enough" indicator to give management the information needed to start or delay additional projects.

This article is provided with permission from the Institute of Industrial and Systems Engineers from the April 2018 issue of ISE, Copyright ©2018 Institute of Industrial and Systems Engineers. All rights reserved.

Success in your first simulation model is crucial for the overall prospects of a new simulation program. Here we will outline the building of your first simulation model with emphasis on the overall process and what it takes to be a successful model builder.

This report shows how process simulation modeling (PSIM) can help companies improve processes and achieve higher levels of process maturity and capability as called for by the Capability Maturity Model® Integration (CMMI®)1 [SEI 2006]. CMMI was developed by a team consisting of members from industry, government, and the Software Engineering Institute (SEI).This report is aimed at practitioners, especially software development project managers, and researchers studying software development processes. The report describes a variety of PSIM applications and discusses how PSIM has helped organizations to improve their implementations of CMMI areas toward higher levels of process capability, maturity and performance.

Letter by letter, the authors summarize 26 of what they consider to be important components for applying knowledge and judgment to simulation problems. They do not claim that these are “the” 26 components. Other simulation analysts could have another set, or they could have multiple components for one letter of the alphabet.

Financial sustainability of social security systems and, more generally, the burden on the state's budget due to the pensions paid to senior citizens depends on the size of the older population and the percentage of insured employees. Credible forecasting of future demgraphic trends could help indicate the optimal retirement age that would put a halt to a growing budget deficit. The paper discusses a simulation model that uses system dynamics to study forecasted demographic changes for a population, based on the aging chain approach.

It has already been proven that there is a strong correlation between demography and savings, and between population aging and economic growth. This paper investigates the life cycle hypothesis and empirically confirms that population aging has the negative influence on a society's savings.

The best known modeling approaches used in the simulation are discrete event simulation (DES) and system dynamics (SD). Discrete rate modeling (DRM) has been proposed by the ExtendSim software manufacturer and combines both approaches but also provides new opportunities. The purpose of the paper is to compare implementation issues in DRM and DES approaches. DRM is particularly useful in high-intensity input streams.

This dissertation contributes to model-based systems engineering (MBSE) by formally defining an MBSE methodology for employing architecture in system analysis (MEASA) that presents a comprehensive framework detailing the relationship between system architecture products and external models and simulations used to analyze system performance and feasibility. Specifically, the research combines the use of Systems Modeling Language (SysML) products and operational simulation models to support assessment of system requirements for systems engineering. The MBSE MEASA transforms operational needs into preferred system configurations through the analysis of detailed simulation models. The research does this by using designed experiments to generate architecture tradespace visualizations that highlight the impact that system design parameters, system-environment interactions, system operational implementation, and system component interactions have on system performance. The research demonstrates a procedure for iterations of the methodology when analysis suggests potentially impactful design, operational, or environmental variables (as well as potential interactions between those variables). The research develops and analyzes notional architecture products and simulation models of United States Navy mine warfare systems to demonstrate an application of the MBSE MEASA.

Simulation models are typically built to obtain an understanding of the system dynamics and compare alternatives. ExtendSim’s Scenario Manager provides an easy interface to evaluate different model configurations and explore the effects of model parameters. Scenario management provides for a systematic, controlled approach to the investigation of a system or process. Among other things, it can be used for problem solving ("what are the main factors contributing to the problem"), parameter design ("how well does the system/process perform given specified factors"), and robustness studies (what is the best configuration of factor values to maximize/minimize variations in response".) Thus it has a very broad application across all disciplines.

Equation-based models and Bayesian networks are not mutually exclusive methods of modeling and simulation. Based on the development of the Netica library for Extend, the two methods can be used simultaneously within the same environment.

In this paper, several models were constructed to demonstrate the utility of this approach with integrated models that contain both equation-based elements and Bayesian networks working together to create a single simulation.

The research also demonstrated the use of complex simulations to train influence diagrams. The examples discussed in this paper demonstrate the feasibility and utility of integrating Bayesian networks and equation- based models. The exploitation of this capability should lead to multiple follow-on research projects. This approach may prove fruitful in a number of scientific disciplines that make use of models and simulations.

This paper applies DOE and broad range sensitivity analysis to a Hybrid System Dynamics and discrete event simulation model of a software development process. DOE is used to analyze the interaction effects, such as the degree to which the impact of the process change depends on worker motivation, schedule pressure and other project environmental variables. The sensitivity of the model to parameter changes over a broad range of plausible values is used to analyze the nonlinear aspects of the model. The end result is a deeper insight into the conditions under which the process change will succeed and improved recommendations for process change design and implementation.

Organizations like NASA and the US Department of Defense make heavy use of Independent Verification and Validation (IV&V) techniques to improve the quality of systems and to reduce the risks associated with the deployment of those systems. Given proper data, software process simulation models (SPSMs) can be used to quantify the costs and benefits associated with both V&V and IV&V practices on software projects, thereby enabling management to more effectively allocate scarce resources for V&V and IV&V activities.

This paper presents a large-scale software development process model that is being developed and used at NASA to apply IV&V at various points in the development process. Preliminary illustrative results show the types of analyses that can be done and point out some of the research issues associated with this work that contributes to mission assurance and success by making recommendations as to how IV&V technologies should be deployed across various projects. These recommendations support planning and management of IV&V and enable IV&V technologies to be applied to software projects more quickly, in order to achieve greater benefits at lower cost.

Lean Simulation Models introduces the use of simulation models as essential tools for lean programs to test alternative solutions before implementing them. Mr. Curry highlights benefits of using Extend and includes screenshots of numerous sample models.

This report shows how process simulation modeling (PSIM) can help companies improve processes and achieve higher levels of process maturity and capability as called for by the Capability Maturity Model® Integration (CMMI®)1 [SEI 2006]. CMMI was developed by a team consisting of members from industry, government, and the Software Engineering Institute (SEI).This report is aimed at practitioners, especially software development project managers, and researchers studying software development processes. The report describes a variety of PSIM applications and discusses how PSIM has helped organizations to improve their implementations of CMMI areas toward higher levels of process capability, maturity and performance.

Consider a single-server queue with a renewal arrival process and generally distributed processing times in which each customer independently reneges if service has not begun within a generally distributed amount of time. A Diffusion Approximation for a GI/GI/1 Queue with Balking or Reneging establishes that both the workload and queue-length processes in this system can be approximated by a regulated Ornstein-Uhlenbeck (ROU) process when the arrival rate is close to the processing rate and reneging times are large. This paper further shows that a ROU process also approximates the queue-length process, under the same parameter assumptions, in a balking model. This balking model assumes the queue-length is observable to arriving customers, and that each customer balks if his or her conditional expected waiting time is too large.