Network Design Optimization
Decide which facilities to open, how to assign customers, and how
product should flow across plants, warehouses, and distribution
centres. PyOpt uses PuLP and Pyomo MILP models with HiGHS, CBC, and
GLPK solvers to minimise total landed cost subject to capacity,
sourcing, and service-level constraints.
Vehicle Routing & Transportation
Solve capacitated vehicle routing problems (CVRP) with Google
OR-Tools. Optimise multi-stop delivery routes, minimise total
distance and driving time, and respect vehicle capacity, time
windows, and depot constraints.
Inventory & Safety Stock
Compute economic order quantities (EOQ), reorder points, and
statistical safety stock from demand and lead-time variability.
Generate per-SKU inventory plans that balance holding cost against
stock-out risk at a target service level.
Sourcing & Procurement Optimization
Allocate demand across suppliers using mixed-integer programming.
Penalise risk and lead-time exposure, enforce supplier capacity
limits, and produce a defensible award decision per product line.
Production Planning
Plan production across lines and shifts, including regular-time and
overtime capacity. Linear-programming models minimise cost while
meeting customer demand and respecting capacity and changeover
constraints.
Scenario Comparison & What-If Analysis
Run side-by-side comparisons of alternative network, sourcing, or
inventory scenarios. Quantify cost, service, and CO₂ deltas to
justify capital projects and operating changes.
Built On Open-Source Optimization
PyOpt is built on a best-of-breed open-source stack:
Pyomo and PuLP for modelling,
HiGHS, CBC, and GLPK
for MIP/LP solving, and Google OR-Tools for
constraint programming and routing. A FastAPI + Celery backend runs
on a private 3-node Hetzner topology with tenant-scoped storage and
row-level security in PostgreSQL.
Get Started
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