There are N workers in a factory. Each worker packs the same type of product, and for each of them we know the time it takes them to pack one product.
Find out the minimum amount of time the workers need to pack at least K products.
Example:
Input: times = [4, 7, 3, 6, 7, 1], K = 60
Output:30
Explanation:
1st worker packs 7 products in 4 * 7 = 28 units of time
2nd worker packs 4 products in 7 * 4 = 28 units of time
3rd worker packs 10 products in 3 * 10 = 30 units of time
4th worker packs 5 products in 6 * 5 = 30 units of time
5th worker packs 4 products in 7 * 4 = 28 units of time
6th worker packs 30 products in 1 * 30 = 30 units of time
We have 7 + 4 + 10 + 5 + 4 + 30 = 60 total products
in max(28, 28, 30, 30, 28, 30) = 30 units of time
and this is the minimum amount of time
since in 29 units of time we can pack only
7 + 4 + 9 + 4 + 4 + 29 = 57 products
The core challenge of this problem is to determine the minimum time required for the workers to pack at least K products. This problem is significant in optimizing production lines and ensuring efficiency in manufacturing processes. A common pitfall is to assume that each worker works independently without considering the collective effort required to meet the target.
To solve this problem, we need to think about how to distribute the work among the workers efficiently. A naive solution would be to simulate the packing process minute by minute, but this is not optimal. Instead, we can use a binary search approach to find the minimum time required.
The naive solution involves iterating through each minute and checking if the workers can pack at least K products. This approach is not optimal because it has a high time complexity.
We can use a binary search to find the minimum time. The idea is to search for the minimum time in a range from 1 to the maximum possible time (which can be the time taken by the slowest worker to pack K products). For each mid-point in the binary search, we calculate the total number of products packed by all workers in that time and adjust our search range accordingly.
Here is a step-by-step breakdown of the binary search algorithm:
def min_time_to_pack_products(times, K):
# Binary search to find the minimum time
low, high = 1, max(times) * K
result = high
while low <= high:
mid = (low + high) // 2
total_products = sum(mid // time for time in times)
if total_products >= K:
result = mid
high = mid - 1
else:
low = mid + 1
return result
# Example usage
times = [4, 7, 3, 6, 7, 1]
K = 60
print(min_time_to_pack_products(times, K)) # Output: 30
The time complexity of the binary search approach is O(n * log(max(times) * K)), where n is the number of workers. The space complexity is O(1) as we are using a constant amount of extra space.
Potential edge cases include:
Each algorithm handles these edge cases by adjusting the search range and calculating the total products accordingly.
To test the solution comprehensively, include a variety of test cases:
Using testing frameworks like unittest or pytest can help automate and validate the test cases.
When approaching such problems, consider the following tips:
In this blog post, we discussed how to find the minimum time required for workers to pack at least K products using a binary search approach. Understanding and solving such problems is crucial for optimizing production processes and improving efficiency. Practice and exploration of similar problems can further enhance problem-solving skills.
For further reading and practice, consider the following resources: