Why does my model find different solutions? Some solutions are better than others – how can that be? How do I find the best solution?

Job sequencing is a common problem. Excel Solver can help you make optimal job sequencing decisions to minimize cost.

This article series implements a staff scheduling model in Excel using OpenSolver, then in Python using Pyomo.

Sometimes we need more power to solve a model. This article describes how to solve a model using the CPLEX solver via the NEOS Server.

We explore methods to decide the best order for positioning devices in a rack: enumerate, heuristic, constraint programming, and linear program.

We replicate a wood cutting pattern model from a published academic paper. Surprisingly, we find a better optimal solution.

Allocating people to teams is a task common in both sport and business. We allocate 32 people to 4 teams that are as balanced as possible.

This series of 13 articles compares Python libraries Pyomo, PuLP, OR-Tools, Gekko, CVXPY, and SciPy for building a linear programming model.

The selection of a better fantasy sports team can substantially improve performance. Excel plus OpenSolver can pick an optimal team.

A new role mining model is claimed to be more tractable and practical. We test that assertion using constraint programming and MILP.

We estimate the speed improvement for optimization solvers in the 35 years from 1989 to 2024. The results may be surprising.

Academic research papers can be tremendously valuable. But only if they are complete. So, academics, please publish your data and code.