This post is mostly to try to get myself back in the habit of posting new blog content. I was looking over some of the examples in the OR-Tools repository, and I started playing around with the tasks and workers assignment problem. The original example can be found here: https://github.com/google/or-tools/blob/master/examples/python/tasks_and_workers_assignment_sat.py. In this post I go through the code bit by bit to understand what is going on, and then try some small changes to see what happens. I hope a newcomer to OR-Tools or CP-SAT might find this useful for starting their own exploration of the examples in the repository.

There was a recent question on the OR-Tools forum that asked how to minimize a passenger’s in-vehicle travel time. I summarized what I had done in the past in my previous entry. The OP replied in the final post in the thread that Mizux’s suggestion to use SetPathEnergyCostOfVehicle() was the best solution. I’ve never used that before, so in this post I am revisiting my previous post using that new function.

There was a recent question on the OR-Tools forum that asked how to minimize a passenger’s in-vehicle travel time. I’ve done this before, but it has been a while, so this post summarizes what I do in similar problems.

A question came up a last fall on the OR-Tools forum about how to enforce a chained pickup and delivery problem. The standard pickup and delivery problem is to pick something up from one place, and then deliver that thing to some other place. This question asked about an interesting and fairly common variation of the standard PDP: a chained sequence of pickups and deliveries.

In my previous post, I looked at how slack variables were used to determine the maximum cost value of a node on a route, and how that value could be assigned as a “cost” for the entire route. The program, vrp_node_max.py. was developed by Mizux and is really clever. However, as I mentioned in my previous post, the final note in the original forum thread from Guy was a note that the approach did not seem to work in practice. With this post, I want to explore what that means and how to fix it.

A few weeks ago while working on a project I came across a very interesting program in the OR-Tools code base. The origin of the code started from a question from Guy on the OR-Tools forum. When the question was originally posted last summer, I didn’t have the opportunity to dig into the proposed result, but now I do, and I discovered it is a very interesting (ab)use of the slack dimension. Since I haven’t written anything here in ages, it seemed like a good idea to write up a blog post on the code and my understanding of how it works.

Recently a question was asked on the OR Tools forum about how to convert a single-day TSP problem into a multi-day TSP problem. The original poster was thorough, and cross posted to Stack Overflow and posted his original code as a gist on GitHub. The problem is interesting in that it takes a away from an idealized lab problem toward integrating real-world constraints. So I responded to the question with my solution, and decided to write it up properly here.

Continuing the theme of writing up my work on optimizing street sweeping routes for Glendale, California, in this post I’m going to talk about how I made an animation to visualize the solutions generated by Google’s OR-Tools.

My last post went into a lot of detail on setting things up to solve edge covering problems. Despite the torrent of words I threw at the topic, I wanted to highlight the step of extracting map data from OpenStreetMap for a city. As with many of my posts, this one is motivated by a question on the OR-Tools mailing list. The original poster asked about generating walking routes to conduct a door-to-door survey of residents of Port-au-Prince, Haiti.

We recently submitted a proposal to optimize street sweeping and garbage collection routes for the city of Glendale, California. This blog post covers some things we learned about setting up the problem using open source data, tools, and libraries.