Simulating the Software Development Lifecycle: The Waterfall Model

(1) Background: This study employs a simulation-based approach, adapting the waterfall model, to provides estimates for projects and individual phase completion times. Additionally, it pin-points potential efficiency issues stemming from a suboptimal resource level. It further demonstrates how one can go on to identify a resource level that effectively eliminates bottlenecks and curtails the idle time of resources. (2) Methods: We implement our software development lifecycle using SimPy, a discrete-event simulation framework written in Python. Our model is executed on 100 projects of varying sizes over three stages. The first, pre-optimization, provides insight based on the initial set of resources. This is followed by the optimization stage, which serves to identify the optimal number of resources to eliminate bottlenecks and minimize idle time. The third stage, post-optimization, evaluates the resource optimized model. (3) Results: The analysis of the simulation-generated data reveals the presence of resource bottlenecks during the pre-optimization stage, particularly in the implementation phase. These dissipate after optimization. (4) Conclusions: The findings emphasize the advantage of using simulation as a safe and effective way to experiment and plan for software development projects. Such simulations also allow those man-aging software development projects to make accurate, evidence-based projections as to phase and project completion times and identify optimal resource levels and their interplay. In particular, understanding the tradeoffs between experiencing delayed completion times and procuring additional resources to alleviate any bottlenecks

Modelling and Simulation of Physical Systems with Dynamically Changing Degrees of Freedom

A new approach is introduced to model and simulate equation-based systems where variables can appear and disappear during simulation without re-generation and re-compilation of code when the numbers of equations and states change during events. The method is presented in a generic, mathematical way and can be in principle applied to all types of declarative, equation-based modelling languages, such as Modelica. A concrete implementation is given for the Julia-based experimental modelling language Modia, which is similar to Modelica. However, Modia features far simpler semantics based on hierarchical collections of name/value pairs and has the ability to support domain-specific algorithms, especially for multibody systems with collision handling. The new method is demonstrated with heat-transfer in a rod, separation of stages of a rocket and gripping operations of a robot

A data-driven approach towards a realistic and generic crowd simulation framework

Jacob Sinclair studied and developed a data-driven approach towards a realistic and generic crowd simulation framework. He found that by using virtual reality and questionnaires, we can gather all types of real world data. He also found that an AI framework developed using all types of data can produce similar results to the real world. This AI framework has the potential to be used to improve areas such as emergency management and response, traffic control, building design, video games, etc

Разработка метода планирования для нефтепродуктообеспечения сети автозаправочных станций : монография

Данная работа посвящена вопросам анализа, планирования и моделирования процессов логистики и цепей поставок. Цепи поставок могут включать в себя производственные процессы и снабжение, изготовление, сбыт продуктов, логистические процессы поставок ресурсов. Организация цепей поставок и системы перевозок является сложной задачей большой размерности, которая фактически сводится к долгосрочному и краткосрочному планированию перевозок, а также оперативному управлению транспортными средствами и решении задачи диспетчеризации. Для их решения применяются численные методы и эвристические подходы. Значительный интерес для решения данных задач представляет исследование возможности применения мультиагентного подхода к задаче планирования перевозок для нефтепродуктообеспечения сети автозаправочных станций

Towards a capability maturity model for a cyber range

This work describes research undertaken towards the development of a Capability Maturity Model (CMM) for Cyber Ranges (CRs) focused on cyber security. Global cyber security needs are on the rise, and the need for attribution within the cyber domain is of particular concern. This has prompted major efforts to enhance cyber capabilities within organisations to increase their total cyber resilience posture. These efforts include, but are not limited to, the testing of computational devices, networks, and applications, and cyber skills training focused on prevention, detection and cyber attack response. A cyber range allows for the testing of the computational environment. By developing cyber events within a confined virtual or sand-boxed cyber environment, a cyber range can prepare the next generation of cyber security specialists to handle a variety of potential cyber attacks. Cyber ranges have different purposes, each designed to fulfil a different computational testing and cyber training goal; consequently, cyber ranges can vary greatly in the level of variety, capability, maturity and complexity. As cyber ranges proliferate and become more and more valued as tools for cyber security, a method to classify or rate them becomes essential. Yet while a universal criteria for measuring cyber ranges in terms of their capability maturity levels becomes more critical, there are currently very limited resources for researchers aiming to perform this kind of work. For this reason, this work proposes and describes a CMM, designed to give organisations the ability to benchmark the capability maturity of a given cyber range. This research adopted a synthesised approach to the development of a CMM, grounded in prior research and focused on the production of a conceptual model that provides a useful level of abstraction. In order to achieve this goal, the core capability elements of a cyber range are defined with their relative importance, allowing for the development of a proposed classification cyber range levels. An analysis of data gathered during the course of an expert review, together with other research, further supported the development of the conceptual model. In the context of cyber range capability, classification will include the ability of the cyber range to perform its functions optimally with different core capability elements, focusing on the Measurement of Capability (MoC) with its elements, namely effect, performance and threat ability. Cyber range maturity can evolve over time and can be defined through the Measurement of Maturity (MoM) with its elements, namely people, processes, technology. The combination of these measurements utilising the CMM for a CR determines the capability maturity level of a CR. The primary outcome of this research is the proposed level-based CMM framework for a cyber range, developed using adopted and synthesised CMMs, the analysis of an expert review, and the mapping of the results