I'm tackling a complex vehicle route optimization problem where I need to create an efficient visit schedule for drivers to several locations. Here's the deal: I have 150 eligible places to visit, but I need to find the best 10 locations that optimize the routes. There are some specific constraints to keep in mind. For instance, I must start and end at home, and on certain days, I specifically need to include locations from a designated area—let's call it Zone A. If there are only 8 places in Zone A, I need to choose 2 additional locations from the remaining 142 that keep the overall route optimized. I might also have time-based constraints, meaning certain locations have to be visited at specific times.
I'm currently utilizing a combination of the 2-opt nearest neighbor algorithm and genetic algorithms to refine the selection, but as it stands, my method doesn't accommodate these constraints. I'm looking for suggestions on how to enhance my approach or resources that can guide me. Also, how would you rate the difficulty of this problem? Is it really that tough, or is it just me? By the way, I've got about 3 years of experience in development and I'm working with data from OpenStreetMap.
3 Answers
Honestly, I felt like I was reading a LeetCode problem for a moment, haha. But really, it’s an interesting challenge! You’re definitely not dumb for finding this tough; these problems can be super tricky.
You might want to consider diving into linear programming for your optimization needs. This type of challenge often falls under the Transportation problem category. Many programming languages have dedicated libraries for handling linear and integer programming. For example, in Python, there's a library you can check out here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.milp.html. Just a heads-up: you'll have to manage some factors like timing outside of the linear programming framework. Also, the intricacies involved in node order and timing turn out to be NP-hard challenges, so take a look at the traveling salesman and knapsack problems for algorithm inspiration. You could also leverage dynamic programming with memoization to find a solid solution.
I've seen similar issues arise in different contexts, and I can tell you, it’s not uncommon to feel overwhelmed by all the constraints and optimizations required. Just keep experimenting with different approaches, and don't hesitate to reach out if you get stuck! I'm sure you'll find a solution.
Thanks for the pointers! I'll definitely look into linear programming.