Documentation overview

This documentation provides a comprehensive guide to understanding and utilizing the features of the qaekwy optimization engine. Whether you’re new to optimization or an experienced user, this documentation will help you get started, explore techniques, and reference the available functionalities.

The documentation is organized into the following sections:

⭐ Introduction

In this section, you will find an introduction to qaekwy and an overview of what this optimization engine can do for you. Whether you’re curious about its capabilities or looking for a high-level understanding, this section will provide you with the necessary fist insights.

🌱 Getting Started

Getting started is the first step towards harnessing the power of optimization. This section will guide you through the installation process, teach you how to design your optimization model, and show you how to solve your first model.

  • Installation: Learn how to install the Qaekwy Python library in your environment.

  • Design Your Model: Get hands-on experience with modelling optimization problems. Understand how to define your first variables, constraints, expressions, searchers, and more.

  • Solve Your Model: Follow step-by-step instructions to submit your model, receive solutions, and interpret the results.

🤖 Guide

The guide section delves deeper into the optimization process, offering guidance on both modelling and solving problems.

  • Modelling: Explore modelling techniques, tips for creating efficient models, and best practices for representing various problem types.

  • Solving: Gain insights into using the DirectEngine to retrieve solutions to your modellings.

📖 Reference

When you need detailed information about the components and functionalities of qaekwy, the reference section is your go-to resource. It provides in-depth explanations of the core elements that make up the Qaekwy Python Library.

  • Variables: Learn how to define and manage variables within your optimization models.

  • Constraints: Understand how to impose constraints on your variables to reflect real-world limitations.

  • Expressions: Explore the various types of expressions that you can use to build complex model relationships.

  • Searchers: Dive into the search algorithms that qaekwy employs to find optimal solutions efficiently.

  • Objectives: Learn how to define optimization objectives and guide the engine towards desired outcomes.

  • Engines: Get acquainted with the different optimization engines supported by qaekwy.

🛠 Examples

Learn by example! This section provides practical use cases and demonstrates how to solve specific optimization problems.

  • Knap Sack Problem: Explore the classic knapsack problem and see how to solve it.

  • Job Scheduling Problem: Learn how to optimize job scheduling scenarios.

  • Facility Location Problem: The Facility Location Problem revolves around determining optimal locations for facilities to serve a set of demand points, aiming to minimize costs while satisfying capacity constraints.

  • Aircraft Fleet Problem: The Aircraft Fleet Problem focuses on selecting an optimal combination of aircraft to maximize a certain objective while adhering to various constraints such as fleet size, distance, and maintenance costs.

❔ About

This section provides additional information, including frequently asked questions and licensing details.

  • FAQ: Find answers to common questions about qaekwy, its capabilities, and usage.

  • License: Learn about the licensing terms and conditions for using qaekwy and this documentation.

Let’s start optimizing!