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.

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.

Keeping with my pattern of posts based on OR Tools GitHub issues, (issue #968) asked how to model having multiple alternatives for pickup locations and dropoff locations.

A question was posed recently on the Google OR Tools GitHub issues (issue #922) that asked whether it was possible to set up FIFO or LIFO constraints. I’d never done that before, but it seemed straightforward to implement. There were a few wrinkles though, so I thought I’d write them up for my future self.

Generating random pickup and delivery pairs has been a pain point for me in the past. The problem comes with generating reasonable destination time windows. In my latest cycle of coding up a test environment for pickup and delivery problems with time windows (PDPTW), I had an insight that both makes generating random deliveries easier, and also improves the actual solution process of the PDPTW problem.

We’ve recently been doing a lot of work with Google’s OR Tools. One of our clients discovered a surprising result while setting disjunctions. As expected, with no disjunctions, all nodes are required. But if you set just one disjunction, most nodes are dropped. We didn’t have a good answer for what was going on, so I decided to explore the situation in this post.

Google’s OR Tools is incredibly useful, but can be tricky to use. I subscribe to the mailing list to keep up on other users’ questions and solutions, and usually I just read and learn. But yesterday a question popped up on the OR-Tools mailing list that I could answer: how to force some node to occur before another node.