Update main.py

main.py

@@ -21,21 +21,21 @@ DEPRECIATION_YEARS = 15
 
 TECHNOLOGY_DATA = {
     "JNC": {"capex_base_M": 180.0, "opex_base_cents_kg": 150.0},
-    # "Hoechst_AG": {"capex_base_M": 230.0, "opex_base_cents_kg": 155.0},
-    # "BF_Goodrich": {"capex_base_M": 280.0, "opex_base_cents_kg": 160.0},
-    # "Shin_Etsu_1991": {"capex_base_M": 260.0, "opex_base_cents_kg": 155.0},
-    # "Shin_Etsu_2004": {"capex_base_M": 1500.0, "opex_base_cents_kg": 150.0},
-    # "Vinnolit": {"capex_base_M": 240.0, "opex_base_cents_kg": 155.0},
-    # "QVC_Qatar": {"capex_base_M": 200.0, "opex_base_cents_kg": 145.0},
-    # "SP_Chemicals": {"capex_base_M": 250.0, "opex_base_cents_kg": 145.0},
-    # "Engro_Pakistan": {"capex_base_M": 1400.0, "opex_base_cents_kg": 155.0},
-    # "Formosa_BR_USA": {"capex_base_M": 380.0, "opex_base_cents_kg": 150.0},
-    # "Shintech_USA_Exp": {"capex_base_M": 1700.0, "opex_base_cents_kg": 160.0},
-    # "Zhongtai_China": {"capex_base_M": 1100.0, "opex_base_cents_kg": 155.0},
-    # "Shintech_USA_2021": {"capex_base_M": 1700.0, "opex_base_cents_kg": 160.0},
-    # "Reliance_India_2024": {"capex_base_M": 2200.0, "opex_base_cents_kg": 155.0},
-    # "Orbia_Germany_2023": {"capex_base_M": 180.0, "opex_base_cents_kg": 155.0},
-    # "Westlake_USA_2022": {"capex_base_M": 850.0, "opex_base_cents_kg": 160.0}
+    "Hoechst_AG": {"capex_base_M": 230.0, "opex_base_cents_kg": 155.0},
+    "BF_Goodrich": {"capex_base_M": 280.0, "opex_base_cents_kg": 160.0},
+    "Shin_Etsu_1991": {"capex_base_M": 260.0, "opex_base_cents_kg": 155.0},
+    "Shin_Etsu_2004": {"capex_base_M": 1500.0, "opex_base_cents_kg": 150.0},
+    "Vinnolit": {"capex_base_M": 240.0, "opex_base_cents_kg": 155.0},
+    "QVC_Qatar": {"capex_base_M": 200.0, "opex_base_cents_kg": 145.0},
+    "SP_Chemicals": {"capex_base_M": 250.0, "opex_base_cents_kg": 145.0},
+    "Engro_Pakistan": {"capex_base_M": 1400.0, "opex_base_cents_kg": 155.0},
+    "Formosa_BR_USA": {"capex_base_M": 380.0, "opex_base_cents_kg": 150.0},
+    "Shintech_USA_Exp": {"capex_base_M": 1700.0, "opex_base_cents_kg": 160.0},
+    "Zhongtai_China": {"capex_base_M": 1100.0, "opex_base_cents_kg": 155.0},
+    "Shintech_USA_2021": {"capex_base_M": 1700.0, "opex_base_cents_kg": 160.0},
+    "Reliance_India_2024": {"capex_base_M": 2200.0, "opex_base_cents_kg": 155.0},
+    "Orbia_Germany_2023": {"capex_base_M": 180.0, "opex_base_cents_kg": 155.0},
+    "Westlake_USA_2022": {"capex_base_M": 850.0, "opex_base_cents_kg": 160.0}
 }
 
 STRATEGY_DATA = {
@@ -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(50)]
     best_overall_individual = None
     best_overall_fitness = -float('inf')
-    for _ in range(10):
+    for _ in range(100):
         fitnesses = [calculate_project_kpis(**ind)['irr'] for ind in population]
         if max(fitnesses) > best_overall_fitness:
             best_overall_fitness = max(fitnesses)
@@ -171,7 +171,7 @@ def run_optuna_direct():
         }
         return calculate_project_kpis(**params)['irr']
     study = optuna.create_study(direction="maximize")
-    study.optimize(objective, n_trials=2, n_jobs=-1)
+    study.optimize(objective, n_trials=200, n_jobs=-1)
     kpis = calculate_project_kpis(**study.best_params)
     return {"Method": "Optuna (TPE - Direct)", **kpis, "Params": study.best_params}