Book Image

Hands-On Simulation Modeling with Python

By : Giuseppe Ciaburro
Book Image

Hands-On Simulation Modeling with Python

By: Giuseppe Ciaburro

Overview of this book

Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.
Table of Contents (16 chapters)
Section 1: Getting Started with Numerical Simulation
Section 2: Simulation Modeling Algorithms and Techniques
Section 3: Real-World Applications

Chapter 10: Modeling and Simulation for Project Management

Sometimes, monitoring resources, budgets, and milestones for various projects and divisions can present a challenge. Simulation tools help us improve planning and coordination in the various phases of the project so that we always keep control of it. In addition, the preventive simulation of a project can highlight the critical issues related to a specific task. This helps us evaluate the cost of any actions to be taken. Through the preventive evaluation of the development of a project, errors that increase the costs of a project can be avoided.

In this chapter, we will deal with practical cases of project management using the tools we learned about in the previous chapters. We will learn how to evaluate the results of the actions we take when managing a forest using Markov processes, and then move on and learn how to evaluate a project using the Monte Carlo simulation.

In this chapter, we're going to cover the following...