✨ Supply Chain Network Optimization

The arcline is a Python framework for building, solving, and analyzing supply chain optimization problems as network flow models. Real supply chains are graphs. Suppliers, plants, warehouses, distribution centers, and customers are nodes; the lanes between them are arcs (or edges); and the optimization questions that matter - where to source, how much to produce, which routes to use, when to open or close a facility - are all decisions about flow on those arcs. The project gives you a declarative API for modeling these networks, a solver-agnostic backend (CBC, HiGHS, Gurobi, CPLEX), and first-class tooling for the parts of the workflow that real practitioners spend most of their time on: data ingestion, scenario comparison, sensitivity analysis, and visualization. It is designed to bridge the gap between the academic clarity of textbook formulations and the messy practical needs of production supply chain teams. Whether you are running a one-off facility location study, building a digital twin of a global distribution network, or embedding a recurring optimization into a daily planning pipeline, arcline aims to be the layer that makes the network the first-class object - and the math, the I/O, and the solver plumbing fade into the background.