Simulation Approach to Life-Data Queue Event Modeling

Simulation process provides a platform to model the real-life scenario from an experimental viewpoint. Simulation plays a key role in providing output that could be used to model the real-life. Queue patterns are studies from two perspective: (1) the stochastic method and (2) simulation technique. This paper spurs on discrete event simulation (DES) technique to investigate the assertions already made in queue model research works. Life-Data already collected from Johnson et al (2018) was used and simulation carried out using simmer package in R language. Findings validate the result of assertions made some research literatures. Keywords: Simulation, Queue, Discrete, Simmer, assertions DOI: 10.7176/CTI/8-0

OUTPUT VISUALIZATION FROM RESULT OF DISCRETE EVENT SYSTEM SIMULATION WITH ‘simmer’ R PACKAGE

This study aims to describe the various capabilities of the simmer package on R, especially in running a discrete event simulation model of a circular system, then develop a DES simulation model building technique, which is effective and can represent real systems well, and explore the simulation output on this simmer, both in statistical summary form and parameter estimation. The method used in this research is the literature study with descriptive and exploratory approaches. Model development is more effective when it is carried out starting from simple models, to more complex forms step by step, and describing the system using a flow chart. Replication for simulations is easy to perform so as to get standard error values ​​for model parameter estimators. The stages in developing a discrete event simulation model with a simmer, start with compiling a simple flowchart to a more complex form, and replication is carried out. The simmer output in the form of a data frame makes it very easy to process the output further. The simple R API on Simmer will also make it easier to simulate

Design and analysis of 5G scenarios with simmer: an R package for fast DES prototyping

Simulation frameworks are important tools for the analysis and design of communication networks and protocols, but they can be extremely costly and/or complex (for the case of very specialized tools), or too naive and lacking proper features and support (for the case of ad-hoc tools). In this article, we present an analysis of three 5G scenarios using simmer, a recent R package for discrete-event simulation that sits between the above two paradigms. As our results show, it provides a simple yet very powerful syntax, supporting the efficient simulation of relatively complex scenarios at a low implementation cost.This article has been partially supported by the 5G-City project (TEC2016-76795-C6-3-R), and the TEXEO project (TEC2016-80339-R), both funded by the Spanish Ministry of Economy and Competitiveness

Using Finite Forkable DEVS for Decision-Making Based on Time Measured with Uncertainty

International audienceThe time-line in Discrete Event Simulation (DES) is a sequence of events defined in a numerable subset of R +. When it comes from an experimental measurement, the timing of these events has a limited precision. This precision is usually well-known and documented for each instruments and procedures used for collecting experimental datas. Therefore, these instruments and procedures produce measurement results expressed using values each associated with an uncertainty quantification, given by uncertainty intervals. Tools have been developed in Continuous Systems modeling for deriving the uncertainty intervals of the final results corresponding to the propagation of the uncertainty intervals being evaluated. These tools cannot be used in DES as they are defined, and no alternative tools that would apply to DES have been developed yet. In this paper, we propose simulation algorithms, based on the Discrete Event System Specification (DEVS) formalism, that can be used to simulate and obtain every possible output and state trajecto-ries of simulations that receive input values with uncertainty quantification. Then, we present a subclass of DEVS models , called Finite Forkable DEVS (FF-DEVS), that can be simulated by the proposed algorithms. This subclass ensures that the simulation is forking only a finite number of processes for each simulation step. Finally, we discuss the simulation of a traffic light model and show the trajectories obtained when it is subject to input uncertainty

Pricing the Cloud: An Adaptive Brokerage for Cloud Computing

Abstract—Using a multi-agent social simulation model to predict the behavior of cloud computing markets, Rogers & Cliff (R&C) demonstrated the existence of a profitable cloud brokerage capable of benefitting cloud providers and cloud consumers alike. Functionally similar to financial market brokers, the cloud broker matches provider supply with consumer demand. This is achieved through options, a type of derivatives contract that enables consumers to purchase the option, but not the obligation, of later purchasing the underlying asset—a cloud computing virtual machine instance—for an agreed fixed price. This model benefits all parties: experiencing more predictable demand, cloud providers can better optimize their workflow to minimize costs; cloud users access cheaper rates offered by brokers; and cloud brokers generate profit from charging fees. Here, we replicate and extend the simulation model of R&C using CReST—an opensource, discrete event, cloud data center simulation modeling platform developed at the University of Bristol. Sensitivity analysis reveals fragility in R&C’s model. We address this by introducing a novel method of autonomous adaptive thresholding (AAT) that enables brokers to adapt to market conditions without requiring a priori domain knowledge. Simulation results demonstrate AAT’s robustness, outperforming the fixed brokerage model of R&C under a variety of market conditions. We believe this could have practical significance in the real-world market for cloud computing. Keywords—CReST; simulation; cloud computing; brokerage I

Exploring the Use of Computer Simulations in Unraveling Research and Development Governance Problems

Understanding Research and Development (R&D) enterprise relationships and processes at a governance level is not a simple task, but valuable decision-making insight and evaluation capabilities can be gained from their exploration through computer simulations. This paper discusses current Modeling and Simulation (M&S) methods, addressing their applicability to R&D enterprise governance. Specifically, the authors analyze advantages and disadvantages of the four methodologies used most often by M&S practitioners: System Dynamics (SO), Discrete Event Simulation (DES), Agent Based Modeling (ABM), and formal Analytic Methods (AM) for modeling systems at the governance level. Moreover, the paper describes nesting models using a multi-method approach. Guidance is provided to those seeking to employ modeling techniques in an R&D enterprise for the purposes of understanding enterprise governance. Further, an example is modeled and explored for potential insight. The paper concludes with recommendations regarding opportunities for concentration of future work in modeling and simulating R&D governance relationships and processes

Patient Pathway Modelling Using Discrete Event Simulation to Improve the Management of COPD

This is an Accepted Manuscript version of 'Usame Yakutcan, Eren Demir, John R. Hurst & Paul C. Taylor (2020) Patient pathway modelling using discrete event simulation to improve the management of COPD, Journal of the Operational Research Society, DOI: 10.1080/01605682.2020.1854626'. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.” Publisher Copyright: © Operational Research Society 2020.The number of people affected by chronic obstructive pulmonary disease (COPD) is increasing and the hospital readmission rate is remarkably high. Therefore, healthcare professionals and managers have financial and workforce-related pressures. A decision support toolkit (DST) for improving the management and efficiency of COPD care is needed to respond to the needs of patients now and in the future. In collaboration with the COPD team of a hospital and community service in London, we conceptualised the pathway for COPD patients and developed a discrete event simulation model (DES) incorporating the dynamics of patient readmissions. A DES model or operational model at this scale has never been previously developed, despite many studies using other modelling and simulation techniques in COPD. Our model is the first of its kind to include COPD readmissions as well as assessing the quantifiable impact of re-designing COPD services. We demonstrate the impact of post-exacerbation pulmonary rehabilitation (PEPR) policy and observe that PEPR would be cost-effective with improvements in quality-adjusted life years (QALYs), reduction in emergency readmissions and occupied bed days. The DST improves the understanding of the impact of scenarios (activities, resources, financial implications etc.) for key decision makers and supports commissioners in implementing the interventions.Peer reviewedFinal Accepted Versio

Discrete Event Simulation for Decision Modeling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening

Markov models are often used to evaluate the cost-effectiveness of new healthcare interventions but they are sometimes not flexible enough to allow accurate modeling or investigation of alternative scenarios and policies. A Markov model previously demonstrated that a one-off invitation to screening for abdominal aortic aneurysm (AAA) for men aged 65 y in the UK and subsequent follow-up of identified AAAs was likely to be highly cost-effective at thresholds commonly adopted in the UK (£20,000 to £30,000 per quality adjusted life-year). However, new evidence has emerged and the decision problem has evolved to include exploration of the circumstances under which AAA screening may be cost-effective, which the Markov model is not easily able to address. A new model to handle this more complex decision problem was needed, and the case of AAA screening thus provides an illustration of the relative merits of Markov models and discrete event simulation (DES) models. An individual-level DES model was built using the R programming language to reflect possible events and pathways of individuals invited to screening v. those not invited. The model was validated against key events and cost-effectiveness, as observed in a large, randomized trial. Different screening protocol scenarios were investigated to demonstrate the flexibility of the DES. The case of AAA screening highlights the benefits of DES, particularly in the context of screening studies