gpt

main.py

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+import json
+from ortools.sat.python import cp_model
+
+
+def solve_task_order(constraints_json):
+    # Parse input
+    prerequisites = []
+    all_tasks = set()
+    for req in constraints_json:
+        # e.g., "Sleep requires dinner"
+        before, _, after = req.partition(" requires ")
+        before = before.strip()
+        after = after.strip()
+        prerequisites.append((before, after))
+        all_tasks |= {before, after}
+    task_list = sorted(all_tasks)
+    task_to_idx = {task: i for i, task in enumerate(task_list)}
+    n_tasks = len(task_list)
+
+    # Model
+    model = cp_model.CpModel()
+
+    # Decision variables: order[t] = position in the sequence of task t
+    order = [model.NewIntVar(0, n_tasks - 1, f"order_{task}") for task in task_list]
+    # All tasks must have distinct positions
+    model.AddAllDifferent(order)
+
+    # We'll maximize the number of prerequisites that are satisfied
+    constraints_satisfied = []
+    for before, after in prerequisites:
+        bidx = task_to_idx[before]
+        aidx = task_to_idx[after]
+        # Boolean variable: is_constraint_ok = (order[a] < order[b])
+        ok = model.NewBoolVar(f"prereq_{before}_after_{after}")
+        model.Add(order[aidx] < order[bidx]).OnlyEnforceIf(ok)
+        model.Add(order[aidx] >= order[bidx]).OnlyEnforceIf(ok.Not())
+        constraints_satisfied.append(ok)
+
+    model.Maximize(sum(constraints_satisfied))
+
+    # Solve
+    solver = cp_model.CpSolver()
+    status = solver.Solve(model)
+    result = []
+    if status in (cp_model.OPTIMAL, cp_model.FEASIBLE):
+        # Build final scheduled order
+        # order[t] = position index, so invert the mapping
+        idx_to_task = {solver.Value(o): task for o, task in zip(order, task_list)}
+        result = [idx_to_task[i] for i in range(n_tasks)]
+    return json.dumps(result)
+
+
+# Example usage:
+if __name__ == "__main__":
+    input_json = [
+        "pizza requires ordering",
+        "sleep requires dinner",
+        "sleep requires toothbrushing",
+        "dinner requires prep",
+        "dinner requires clean_dining_room",
+        "prep requires shopping",
+        "shopping requires money",
+        "ordering requires money",
+        "money requires work",
+        "work requires waking_up",
+        "clean_dining_room requires cleaning_time",
+    ]
+    output_json = solve_task_order(input_json)
+    print(output_json)