Update main.py

main.py

@@ -50,7 +50,7 @@ PRODUCT_PRICES_USD_PER_TON = {
 
 OPTIMIZATION_SPACE = {
     'capacity_kta': (500, 600),
-    'technology': ["Engro_Pakistan"], #, "Shin_Etsu_2004"],
+    'technology': ["Engro_Pakistan", "Shin_Etsu_2004"],
     'sourcing_strategy': ['Integrated_Production'],
     'export_market_mix': (0.6, 0.8),
     'sell_byproducts': [True]
@@ -125,7 +125,7 @@ def run_genetic_algorithm():
     population = [{k: random.choice(v) if isinstance(v, list) else random.uniform(*v) for k,v in OPTIMIZATION_SPACE.items()} for _ in range(40)]
     best_overall_individual = None
     best_overall_fitness = -float('inf')
-    for _ in range(70):
+    for _ in range(80):
         fitnesses = [calculate_project_kpis(**ind)['irr'] for ind in population]
         if max(fitnesses) > best_overall_fitness:
             best_overall_fitness = max(fitnesses)
@@ -154,7 +154,7 @@ def run_bayesian_optimization():
     @use_named_args(skopt_space)
     def objective(**params):
         return -calculate_project_kpis(**params)['irr']
-    res = gp_minimize(objective, skopt_space, n_calls=80, random_state=42, n_initial_points=20)
+    res = gp_minimize(objective, skopt_space, n_calls=90, random_state=42, n_initial_points=20)
     best_params = {space.name: val for space, val in zip(skopt_space, res.x)}
     kpis = calculate_project_kpis(**best_params)
     return {"Method": "Bayesian Opt", **kpis, "Params": best_params}