You might have a look at, https://juliacomputing.com/case-studies/
I don’t have a specific write up but I can say anicotically that a significant chunk of my job is developing handcrafted heuristics for specific types of optimization problems, both for publication and deployment. One thing I found really wonderful about Julia is that I can start developing high-level algorithmic ideas with minimal engineering overheads (e.g. ignore types, memory allocation, ect…). Once the algorithm design is solidified I used Julia’s benchmarking tools I can identify the core bottlenecks and improve the performance as-needed with typing and other code optimization tricks. In my experience, very carefylly written Julia can be comparable to C++ in performance.
This work flow is very different than my past experience when I would first develop in python (or similar) and then have to start over with C++ once we knew what algorithm needed to be built. Sticking in one language and only focusing on the core bottlenecks saves me lots of time.
I am also a big fan of the JuMP modeling layer for optimization. I very often use JuMP models as a sub-problem in more complex heuristics and the JuMP overheads are almost never the bottleneck in my workflows.