With the wide array of programming languages available today, developers can often feel overwhelmed when deciding which one to use for a given project. While many languages share common principles, they also differ significantly in functionality, performance, and ecosystem. Python is one of the most widely adopted languages, valued for its readability, ease of use, and the strength of its community and libraries. Rust, being a compiled language, offering significant benefits in terms of speed and memory safety—albeit with a steeper learning curve.
Selecting a language is typically influenced by a developer's familiarity, but the complexity of the problem at hand can often drive a need to look beyond the usual tools. In industrial environments—ranging from manufacturing to logistics—addressing daily challenges such as production planning and demand forecasting requires efficient algorithms and reliable execution. One popular mathematical approach to model such scenarios is Mixed Integer Programming(MIP), which involves solving optimization problems with both integer and continuous variables.
MIP problems are computationally intensive due to their combinatorial nature. As the number of integer variables and constraints grows, so does the complexity of the solution space—especially in time-sensitive or real-time applications.
This blog walks through our experience building a parallel MIP solver, beginning with Python for prototyping and later incorporating Rust. This shift illustrates how combining the strengths of different languages can unlock performance gains, and why stepping outside the comfort zone of familiar tools can be worth while when developing enterprise-grade applications.
The core method typically used to solve MIPs (Mixed Integer Programs) is an algorithm called Branch and Bound (B&B). Many commercial and open-source solvers use the Branch and Bound algorithm to systematically explore all possible solutions to the MIP by branching, bounding on the ones that are integer feasible and pruning (eliminating) the unwanted branches. But the implementations of B&B provided by these solvers are not typically flexible or adaptable for controlled experimentations or custom use-cases.
Therefore, we developed our very own lightweight, flexible MIP solver with our bespoke parallel B&B tree structure and management approach that could be modified, tested, and evaluated in isolation.
Fig. 1 shows the schematic of the Hub-multi worker parallel B&B architecture. Learn more about our parallel architecture in our next blog.
Harnessing Rust’s Safety and Speed: A Strategic Choice for Scalable Parallel Branch and Bound Algorithms
Our MVP (Minimal Viable Product) was initially built in Python mainly for its suitability to agile and rapid development and its extensive machine learning libraries. Python’s flexibility allowed us to prototype and test ML-based variable selection strategies quickly, leveraging existing libraries such as Scikit-learn to validate our approach. Read more about our ML based Branching Variable Selection here. .
However, as our focus shifted toward parallel development and the need for a highly efficient and scalable system, we transitioned to Rust for the following main reasons:
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Two phased development approach of our MVP provided an alternate implementation to benchmark and compare our advanced Rust-based system against, ensuring that the improvements in scalability and efficiency were both measurable and meaningful.
Our two-phased approach—from a Python MVP to a Rust implementation—reflects agile principles, balancing rapid prototyping with a transition to a robust, high-performance solution. While our goal wasn't a detailed performance benchmark, comparing the Python and Rust versions of our Branch-and-Bound framework highlighted practical reasons for the switch. Python was ideal for quickly testing ML strategies and building an MVP, but as our needs grew to include parallelism, scalability, and efficient resource handling, Rust proved better suited for production-grade development.
We ran the tests on both Python and Rust versions of our Parallel B&B MIP solver with the following configuration settings:
For testing purpose, we chose 10 problems of varying difficulty levels which are part of the publicly listed collection set of MIPLIB 2017.
The following table summarises the results in a nutshell
In the above graph we can observe that the Rust solver has found better or same feasible solutions for every instance, and improved bounds r in 8/10 instances. In the remaining two benchmarks, the Rust solver was terminated as a gap of 1% was reached.
The speed of the rust implementation was almost 6-7 times faster than that of Python’s. The average MIP gap of the solutions found by the Rust solver is 2.5 times smaller than the Python implementation and further validates our decision to proceed with Rust as our preferred stack for our parallel B&B solver implementation.
As we can observe in the above graph for one of the instances (mik-250-20-75-4), Python failed to find any feasible solution whereas Rust managed to find a solution with 7.8% gap, demonstrating that the improved performance can help to solve harder MIPs.
We compared the performance of our bespoke Branch-and-Bound solver in both Python and Rust. While Python enabled rapid prototyping and ML strategy testing, it struggled with runtime and memory efficiency on larger instances. In contrast, the Rust version—leveraging low-level control and concurrency—achieved 6–7× faster execution with significantly lower memory usage. Though not a comprehensive benchmark, these results clearly demonstrated Rust’s suitability for scalable, high-performance, parallel computation. This performance gain, along with Rust’s safe concurrency model, validated our shift to Rust for production while retaining the Python prototype for ongoing ML experimentation.
