Buddy Restaurant - Find the Nearest Restaurant Pairing for Joint Delivery

Question

## Round 1 - Coding

## Problem

DoorDash is testing a "buddy delivery" feature where a single dasher picks up from two nearby restaurants and delivers both orders in one trip. Given a list of restaurants with coordinates and a list of pending orders (each tied to a restaurant), find the best restaurant pairing for a dasher at a given location: the pair that minimizes the total travel distance (dasher -> restaurant A -> restaurant B -> customer).

```python
def best_buddy_pair(
    dasher_location: tuple[float, float],
    restaurants: list[dict],  # {"id": int, "x": float, "y": float}
    orders: list[dict],       # {"order_id": int, "restaurant_id": int, "customer_x": float, "customer_y": float}
) -> tuple[int, int]:  # (order_id_1, order_id_2)
    # Returns the pair of order IDs whose combined route is shortest
    # Route: dasher -> R1 -> R2 -> midpoint of both customers
    ...

def euclidean(a: tuple, b: tuple) -> float: ...
```

```
Example:
dasher = (0, 0)
restaurants = [{"id": 1, "x": 1, "y": 0}, {"id": 2, "x": 0, "y": 1}]
orders = [
    {"order_id": 10, "restaurant_id": 1, "customer_x": 3, "customer_y": 0},
    {"order_id": 11, "restaurant_id": 2, "customer_x": 0, "customer_y": 3},
]
best_buddy_pair(...) -> (10, 11)
```

## Follow-ups

1. How do you decide the visit order (R1 then R2, or R2 then R1)?
2. For N orders, naive pairing is O(N^2) - what constraints make this acceptable in practice?
3. How would you add a constraint that both deliveries must complete within 30 minutes?
4. How does the routing change if the customer locations must each be visited separately?

Buddy Restaurant - Find the Nearest Restaurant Pairing for Joint Delivery

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