feat: add objective factors/mode defaults and validation

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

solve.py

@@ -67,6 +67,32 @@ FIXED_CHOICES = {
 #   lambda res: res["E"][3] >= 50  # Ensure at least 50 E at step 3
 RESOURCE_CONSTRAINTS = []
 
+# Maximize criteria for solve(). Factor = exponent in "product" mode,
+# weight in "sum" mode. Keys: E, B, S, C; missing keys = 0 (resource
+# excluded from the objective — it is NOT forced to zero). Negative
+# factors are allowed only in "sum" mode (a negative exponent would mean
+# division, which integer CP-SAT cannot express).
+OBJECTIVE_FACTORS = {"E": 2, "B": 1, "S": 2}
+OBJECTIVE_MODE = "product"  # "product" or "sum"
+
+
+def _validate_objective(factors, mode):
+    if mode not in ("product", "sum"):
+        raise ValueError(f"objective_mode must be 'product' or 'sum', got {mode!r}")
+    unknown = set(factors) - {"E", "B", "S", "C"}
+    if unknown:
+        raise ValueError(f"unknown objective_factors keys: {sorted(unknown)}")
+    for k, v in factors.items():
+        if not isinstance(v, int) or isinstance(v, bool):
+            raise ValueError(f"objective factor {k}={v!r} must be an int")
+        if mode == "product" and v < 0:
+            raise ValueError(
+                f"objective factor {k}={v} is negative; negative exponents "
+                "are not expressible in product mode"
+            )
+    if not any(factors.values()):
+        raise ValueError("objective_factors needs at least one nonzero factor")
+
 # Arrival schedule. Key = step (1..5), value = list of city types that arrive
 # at the START of that step. Types: 'H' Hub, 'F' Foundry, 'M' Metropolis, 'N' Monument.
 # Arriving cities act that same step.