This repository contains a functional AIMMS example for the classic Knapsack Problem. It demonstrates how to select an optimal subset of items to maximize total value without exceeding the maximum weight capacity of the knapsack.
The knapsack problem is a fundamental optimization challenge used in resource allocation, portfolio selection, and loading scenarios. This model helps address:
- Value Maximization: Selecting items that provide the highest accumulated return.
- Capacity Constraints: Respecting strict weight limits of the container.
- Quantity Management: Handling variants where items can be unique (Binary), limited (Bounded), or unlimited (Unbounded).
To get the most out of this model, we highly recommend reading our detailed step-by-step guide on the AIMMS How-to website:
👉 Read the Full Article: Knapsack Problem Guide
- AIMMS: You will need AIMMS installed to run the model. Download the Free Academic Edition here if you are a student.
- WebUI: This model is optimized for the AIMMS WebUI for a modern, browser-based experience.
- Dynamic Solving: A single algebraic model that handles three types of solve (Classic, Unbounded, and Bounded) by dynamically modifying variable bounds.
- Data Integration: Uses the AXLL library for seamless Excel import and export of item data.
- Randomization: Includes procedures to randomize data sets for testing and playful exploration.
- WebUI: This model features advanced UI widgets, including Sliders, Multiselectors, and data-dependent CSS styling for an interactive experience.
- Download the Release: Go to the Releases page and download the
.zipfile from the latest version. - Open the Project: Launch the
.aimmsfile. - Run the Model: Use the WebUI workflow to randomize data, import from Excel, and solve using different knapsack variants.
This example is maintained by the AIMMS User Support Team.
- Found an issue? Open an issue.
- Questions? Reach out via the AIMMS Community.
Maintained by the AIMMS User Support Team. We optimize the way you build optimization.