Operations research methods tuned for real‑world logistics and industrial engineering.
We frame core planning as LP/MIP: assignment, knapsack, cutting‑stock, and facility‑location variants. Solved with efficient branch‑and‑bound and cutting planes on bounded problem sizes.
For VRP with time windows and capacities, we use constructive methods (sweep, savings), then improve with local search (2‑opt, 3‑opt), large neighborhood search, and guided tabu search.
We combine first‑fit/ best‑fit decreasing with geometric checks to respect weight distribution, cube limits, and stacking rules for pallets, trailers, containers, and ULDs.
Greedy and dynamic programming to schedule docks, appointments, and crew within service windows, with penalties for early/late arrivals and soft constraints when needed.
Objective blends cost, distance, lateness, utilization, and CO₂. We normalize, weight per user preferences, and use Pareto‑efficient moves during search to balance trade‑offs.
Buffers for dwell and traffic variability; scenario sampling to keep plans stable under realistic fluctuations without being overly conservative.
Every plan exposes the constraint set, scoring breakdown, and the improving moves applied, so planners can audit and adjust with confidence.
Compute budgets are tuned for fast responses on Cloudflare Workers. We cap instance sizes, use incremental search, and cache reusable subresults where appropriate.
Dive into our LP/MIP models, VRP heuristics, and edge architecture.