[yorak]

This is an interesting and promising take on the problem. Despite being introduced already in the 60'ies, the optimization of delivery routes is still not used as widely as it should. I'd argue that this is mostly due to the complexities and challenges inherent in adapting such optimization technology to solve real world delivery route planning tasks, and, on the other hand, the high cost and low availability of operations researchers with relevant software engineering background.

In my recent PhD dissertation I tried to address the challenges from a different angle: I proposed using machine learning to predict the most suitable heuristic algorithm and its parameter values for a specific logistics planning problem. This way the developer or the user does not need to worry about the details of the optimization solver. The book is freely available for download from: https://jyx.jyu.fi/handle/123456789/65790

[ryan-nextmv]

Thank you so much for sharing this! I will read it.

I suspect there's a lot of potential for these sorts of techniques in production systems, especially combined with decision diagrams. We've been looking at DDs partly because they are capable of optimizing problems without a lot of restrictions on problem type or representation, and also because they seem like a very nice metaheuristic structure.