This tutorial talks about the Interval scheduling algorithm presents a Python version of it and shares its applications in real-life.

## 1. What is The Interval Scheduling Algorithm?

The interval scheduling algorithm is a greedy algorithm for finding the maximum number of non-overlapping intervals from a set of intervals. The problem is known as the maximum independent set of intervals problem and is commonly used in scheduling, resource allocation, and other optimization problems.

The algorithm works by sorting the intervals in order of their end times, and then iteratively selecting intervals that do not overlap with any previously selected intervals. The end time is used as the key because it represents the earliest possible point at which another interval can be scheduled. The algorithm can be implemented as follows:

- Sort the intervals by their end times.
- Initialize an empty list to store the selected intervals.
- Set a variable “current_time” to the start time of the first interval.
- For each interval in the sorted list of intervals: a. If the start time of the interval is greater than the current_time, add the interval to the list of selected intervals and update the current_time to be the end time of the interval. b. If the start time of the interval is less than or equal to the current_time, discard the interval.
- Return the list of selected intervals.

The time complexity of the interval scheduling algorithm is O(n log n), where n is the number of intervals, due to the sorting step. The algorithm is guaranteed to find an optimal solution and is a simple and efficient solution to the maximum independent set of intervals problem.

## 2. Advantages of the Interval Scheduling Algorithm Over Other Algorithms

The interval scheduling algorithm has several advantages over other algorithms:

- Greedy Approach: The interval scheduling algorithm is a greedy algorithm, which means it makes the best choice at each step and builds a solution incrementally. This makes the algorithm fast and efficient.
- Optimal Solution: The algorithm is guaranteed to find the optimal solution to the maximum independent set of intervals problem, meaning it will find the maximum number of non-overlapping intervals.
- Simplicity: The algorithm is simple and easy to understand, making it a good choice for educational or introductory applications.
- Flexibility: The algorithm can be easily adapted to handle variations of the problem, such as minimizing the total duration of the selected intervals or maximizing the total weight of the selected intervals.
- Efficient: The time complexity of the algorithm is O(n log n), where n is the number of intervals, making it efficient for large sets of intervals.
- Well-studied: The interval scheduling algorithm is a well-studied and well-understood algorithm, with a proven track record of success in a variety of applications.

In summary, the interval scheduling algorithm is a simple, efficient, and effective solution to the maximum independent set of intervals problem. Its greedy approach, optimal solution, and well-studied nature make it a good choice for many applications.

## 3. The Interval Scheduling Algorithm Use in Real-Life Problems

The interval scheduling algorithm can be used to solve a variety of optimization problems that involve scheduling or allocating resources to a set of tasks, where each task is represented as an interval. Some common problems that can be solved using the interval scheduling algorithm include:

- Task Scheduling: The algorithm can be used to schedule a set of tasks with known start and end times, such that no two tasks overlap.
- Resource Allocation: The algorithm can be used to allocate a shared resource, such as a machine, to a set of tasks, such that the resource is used as efficiently as possible.
- Meeting Scheduling: The algorithm can be used to schedule a set of meetings with known start and end times, such that no two meetings overlap.
- Job Shop Scheduling: The algorithm can be used to schedule a set of jobs in a job shop, where each job has a set of tasks that must be performed in a specific order on different machines.
- Course Scheduling: The algorithm can be used to schedule a set of courses with known start and end times, such that no two courses overlap.
- Facility Scheduling: The algorithm can be used to schedule the use of a facility, such as a conference room or sports field, for a set of events with known start and end times.
- Interval Partitioning: The algorithm can be used to partition a set of intervals into disjoint subsets, where each subset represents a set of non-overlapping intervals.

In general, the interval scheduling algorithm can be used to solve problems where a set of intervals must be assigned to a set of resources in an optimal manner. Its simplicity, efficiency, and guaranteed optimal solution make it a useful tool in a variety of scheduling and resource allocation problems.

## 4. Python Implementation of the Interval Scheduling Algorithm

Here we present the Python solution proposed by techiedelight:

```
# A class to store a Job
class Job:
def __init__(self, start, finish, profit):
self.start = start
self.finish = finish
self.profit = profit
# Function to perform a binary search on the given jobs, which are sorted
# by finish time. The function returns the index of the last job, which
# doesn't conflict with the given job, i.e., whose finish time is
# less than or equal to the given job's start time.
def findLastNonConflictingJob(jobs, n):
# search space
low = 0
high = n
# iterate till the search space is exhausted
while low <= high:
mid = (low + high) // 2
if jobs[mid].finish <= jobs[n].start:
if jobs[mid + 1].finish <= jobs[n].start:
low = mid + 1
else:
return mid
else:
high = mid - 1
# return the negative index if no non-conflicting job is found
return -1
# Function to find the maximum profit of non-overlapping jobs using DP
def findMaxProfit(jobs):
# base case
if not jobs:
return 0
# sort jobs in increasing order of their finish times
jobs.sort(key=lambda x: x.finish)
# get the number of jobs
n = len(jobs)
# construct a lookup table where the i'th index stores the maximum profit
# for the first `i` jobs
maxProfit = [None] * n
# maximum profit gained by including the first job
maxProfit[0] = jobs[0].profit
# fill the `maxProfit` table in a bottom-up manner from the second index
for i in range(1, n):
# find the index of the last non-conflicting job with the current job
index = findLastNonConflictingJob(jobs, i)
# include the current job with its non-conflicting jobs
incl = jobs[i].profit
if index != -1:
incl += maxProfit[index]
# store the maximum profit by including or excluding the current job
maxProfit[i] = max(incl, maxProfit[i - 1])
# return maximum profit
return maxProfit[n - 1]
if __name__ == '__main__':
jobs = [
Job(0, 6, 60), Job(1, 4, 30), Job(3, 5, 10),
Job(5, 7, 30), Job(5, 9, 50), Job(7, 8, 10)
]
print('The maximum profit is', findMaxProfit(jobs))
```

## 5. Conclusions

In conclusion, this tutorial has provided a comprehensive overview of the Interval scheduling algorithm and its applications in real-life. By explaining the concept of interval scheduling and the greedy algorithm that solves it, users can understand how to efficiently schedule tasks within a given timeframe. Additionally, the tutorial provides a Python implementation of the algorithm, which can be easily customized and integrated into various projects. With the interval scheduling algorithm, users can optimize their scheduling and resource allocation strategies, whether it’s for scheduling appointments, managing resources, or any other similar real-life application. By utilizing this algorithm, users can save time and resources and improve their overall efficiency. Overall, this tutorial is a valuable resource for anyone looking to learn about interval scheduling and how to apply it in real-life situations.