Tutorials
Python AhpAnpLib Library
List of available tutorials
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Tutorial 00: Installing Python AhpAnpLib and Tools needed on Windows/Mac
In this tutorial series, we will guide you through the process of installing the essential tools needed on a Mac/Windows system to work with the Python AhpAnpLib library first. Learn how to set up Visual Studio Code and Python. Then, we will show you how to install and validate the latest version of the Python AhpAnpLib library. Follow along as we guide you through the installation process step-by-step, ensuring you have the most up-to-date tools for creating AHP/ANP models.
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Tutorial 01: Building a hierarchical pairwise comparison model
In this tutorial series, we will guide you through the process of creating an Analytic Hierarchy Process (AHP) model using the Python AhpAnpLib library through various methods: from scratch, Excel, and SuperDecisions .sdmod files. We'll delve into the step-by-step process of building the AHP model in these tutorials. You'll learn how to set up the hierarchical models, export pairwise comparison questionnaire templates, elicit judgments using Excel templates, and calculate results.
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Tutorial 02: Creating a ratings model
In this tutorial, we will guide you through the process of creating a Ratings Model using the example of car selection. You will learn how to assign numerical values to pairwise comparisons, how to add ratings criteria, using ratings scales to rate alternatives reflecting the preferences and priorities of decision-makers. By establishing these ratings, you'll gain valuable insights into the relative significance of criteria and alternatives within your decision model.
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Tutorial 03: Creating an ANP model
To be published.
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Tutorial 04: Creating BOCR Models
In this tutorial we delve into creating Benefits, Opportunities, Costs, and Risks (BOCR) models using Python and calculating short-term and long-term best decisions using an Excel template to facilitate the integration process and calculating the final decisions.
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Tutorial 05: Optimization with AHP (Resource Allocation)
In this tutorial we focus on resource allocation by combining the structured approach of AHP with the computational power of optimization techniques. We will guide you through the integration of AHP with optimization methods, demonstrating how to formulate decision variables, constraints, and objectives based on the priorities derived from the AHP analysis.