A concise overview of our optimization approach and edge‑native design.
Easimplex combines linear/mixed‑integer programming with practical heuristics to deliver fast, explainable plans for real‑world logistics and industrial engineering workloads.
We formulate assignment, knapsack, cutting‑stock variants as LP/MIP with bounded instance sizes. We use presolve, cutting planes, and branch‑and‑bound tuned for common operations constraints.
For VRP with time windows and capacities, we employ constructive methods (sweep, savings), local improvements (2‑opt/3‑opt), and large‑neighborhood search guided by multi‑objective scores.
Workers execute within strict compute budgets. We cap instance sizes, cache reusable subresults, and incrementally improve solutions to maintain low latency on Cloudflare.
Plans expose constraint sets, scoring breakdowns, and improving moves so planners can audit and adjust with confidence.
We minimize retained data, isolate per‑tenant storage, and apply least‑privilege access. Email verification and manual approvals reduce abuse by default.