Solver Max - Practical optimization: A gentle introduction

Optimization 101 is an online textbook written by Distinguished Research Professor Emeritus John W. Chinneck.

Topics include:

Linear programming.
Networks.
Integer programming.
Heuristics.
Dynamic programming.
Non-linear programming.

Textbook: Practical optimization: A gentle introduction.

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Crossword puzzles are a popular pastime. We design and build Mixed Integer Linear Programming models to compile crossword puzzles.
Optimal facility location is a common & difficult decision that organizations need to make. We build a location optimization model in Excel.
How many other optimal solutions exist? How do I find those solutions. This article explains how to use the CPLEX solution pool.
We compare a linear programming model written using Julia/JuMP with the same model written using Python/Pyomo.
Designing an optimization model is difficult. This article explains common mathematical notation used to define optimization model formulations.
A new role mining model is claimed to be more tractable and practical. We test that assertion using constraint programming and MILP.
This series explores optimization of a "picking warehouse" by improving the warehouse design and efficiency of the picking process.
To compare optimization modelling in Excel and Python, we replicate a Python model then compare it with an equivalent Excel implementation.
Bin packing is a common problem. A new formulation helps us solve a simple, yet difficult to solve, bin packing model.
This article replicates and extends a transportation optimization model for managing a COVID-19 vaccination plan in Hong Kong.
We describe an example of how to represent price breaks in a linear programming model, built in Excel using Solver or OpenSolver.
We report our experience of Copilot coding a non-trivial optimization model, focussing on what went well and what didn't go well as the model evolves.

Practical optimization: A gentle introduction

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