adventures in optimization

I posted a couple years ago about my adventures applying various graph visualization tools to Decision Diagrams (DD).

Some years ago, I worked on real-time meal delivery at Zoomer, a YC startup based out of Philadelphia.

I build decision science and optimization software.

I just returned from the 2025 INFORMS Computing Society conference, where I had the privilege of organizing a cluster on optimization solvers.

In the last post, we used Gurobi’s hierarchical optimization features to compute the Pareto front for primary and secondary objectives in an assignment problem.

One of the first technology choices to make when setting up an optimization stack is which modeling interface to use.

At a Nextmv tech talk a couple weeks ago, I showed a least absolute deviations (LAD) regression model using OR-Tools.

I attended DPSOLVE 2023 recently and found lots of good inspiration for the next version of Nextmv’s Decision Diagram (DD) solver, Hop.

Note: This post has been updated to work with HiGHS.

Note: This post was originally written using Julia v0.2, GLPK, and Hedonometer data through 2014.

In the previous post, we considered preprocessing for the vehicle routing problem where the vehicles have different starting locations.

Consider an instance of the vehicle routing problem in which we have drivers that are geographically distributed, each in a unique location.

Note: This post was written before Gurobi supported nonlinear optimization.

Note: A reader pointed out that Union-Find is a very efficient way to accomplish this task.

Note: This post was updated to work with Python 3 and the 2nd edition of “Integer Programming” by Laurence Wolsey.

Note: This post was updated to work with Python 3 and PySCIPOpt.

Note: This post was updated to work with Python 3 and PySCIPOpt.

In response to this post, Ben Bitdiddle inquires: I understand the concept of using a companion set to remove duplicates from a list while preserving the order of its elements.

This is based on a lightning talk I gave at the LA PyLadies October Hackathon.

I find I have to build simulations with increasing frequency in my work and life.

Note: This post was updated to work with NetworkX and for clarity.

I recently stumbled across an implementation of the affine scaling interior point method for solving linear programs that I’d coded up in R once upon a time.

Note: This post was edited for clarity.

Note: This post was edited for clarity.

Note: This post was edited for clarity.

Note: This post was updated to work with Python 3.12.

Note: This post was updated to work with Python 3.12.

Here’s the second in my reformed JAPH series.

Note: This post was edited for clarity.

I hope you saw “China’s way to the top” on the Post’s website recently.

Note: This post was updated to include an example data file.

This post is based on a memo I sent to some former colleagues at the Post.

Consider this scenario: You run a contest that accepts votes from the general Internet population.

Note: This post was updated to work with Python 3 and PySCIPOpt.

Note: This post was updated to work with Python 3.

One of the difficulties we encounter in solving the Traveling Salesman Problem (TSP) is that, for even a small number of cities, a complete description of the problem requires a factorial number of constraints.

Uncapacitated Lot Sizing (ULS) is a classic OR problem that seeks to minimize the cost of satisfying known demand for a product over time.