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 7: Using Simulation to Improve and Optimize Systems

Simulation models allow us to obtain a lot of information using few resources. As often happens in life, simulation models are also subject to improvements in order to increase their performance. Through optimization techniques, we try to modify the performance of a model to obtain improvements both in terms of the results and when trying to exploit resources. Optimization problems are usually so complex that a solution cannot be determined analytically. Complexity is determined first by the number of variables and constraints, which define the size of the problem, and then by the possible presence of non-linear functions. To solve an optimization problem, it is necessary to use an iterative algorithm that, given a current approximation of the solution, determines, with an appropriate sequence of operations, updates to this approximation. Starting from an initial approximation, a sequence of approximations that progressively...