How do you solve Average Waiting Time on LeetCode?

Average Waiting Time (LeetCode #1701) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(1) space complexity. Key insight: Understanding how to track time is crucial in simulation problems.

What is the brute force solution for Average Waiting Time?

The brute force approach for Average Waiting Time is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach simulates each customer's waiting time by checking when the chef finishes preparing each order. It calculates the waiting time for each customer individually and then averages them. This method is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Average Waiting Time?

Average Waiting Time uses the following concepts: Array, Simulation. The recommended patterns to study are: Simulation, Greedy.

What are the interview tips for Average Waiting Time?

Always clarify the order of operations before coding. Consider edge cases, such as when customers arrive at the same time. Practice simulating the problem with small examples to build intuition.

What are common mistakes when solving Average Waiting Time?

Forgetting to update the current time based on the customer's arrival. Not correctly calculating the waiting time by mixing up the order of operations.

#1701

Average Waiting Time

Medium

ArraySimulationSimulationGreedy

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(1)

💡

Intuition

Time O(n)Space O(1)

The optimal solution efficiently calculates the average waiting time by tracking when the chef is free and when each customer arrives. It avoids unnecessary checks and directly computes waiting times in a single pass through the customers.

⚙️

Algorithm

5 steps

1Step 1: Initialize total waiting time and current time to 0.
2Step 2: Iterate through each customer in the list.
3Step 3: If the chef is free (current time < arrival), set current time to the customer's arrival time.
4Step 4: Calculate the waiting time for the customer and add it to the total waiting time. Update the current time by adding the preparation time of the customer.
5Step 5: After processing all customers, return the average waiting time.

solution.py8 lines

1def averageWaitingTime(customers):
2    total_waiting_time = 0
3    current_time = 0
4    for arrival, time in customers:
5        current_time = max(current_time, arrival)
6        total_waiting_time += current_time - arrival + time
7        current_time += time
8    return total_waiting_time / len(customers)

ℹ

Complexity note: This complexity is linear because we only make a single pass through the list of customers, performing constant time operations for each customer.

1Understanding how to track time is crucial in simulation problems.
2Using max(current_time, arrival) helps to efficiently manage waiting times.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.