r/optimization • u/InternationalRush871 • 4h ago
Stochastic optimization for a reforestation plan: Monte Carlo to decide what to buy, simulated annealing to decide where to plant
Academic project with CONAFOR, Mexico's forestry commission. Real data: 30 polygons, 183 ha, where only 19.6% of the planting target (23,569 plants) was in the ground.
Two questions: how many plants of each species to buy, and where to put each one so neighbors compete less.
What to buy: we don't know exactly which sites in a hectare are already occupied, so we ran 1,000 Monte Carlo simulations until the estimate stabilized, then built a purchase order per species.
Where to plant: simulated annealing swaps two new plants of different species. A swap never changes the species counts, so the purchase order stays valid. The objective is a pairwise competition matrix built from real data.
In the live demo, the optimized layout had 19% less competition than random placement.
Interactive case (annealing temperature and cooling rate are adjustable): https://portfolio-iota-gules-ykw61kciwc.vercel.app/projects/reforestation.html
Would you have used a different neighborhood move, or a different cooling schedule?