Job Scheduling Optimization Using UCS and A* Search Algorithms

This project focuses on solving the Job Scheduling optimization problem using search algorithms from the field of Artificial Intelligence. The goal is to determine the optimal order of executing multiple jobs while minimizing the total penalty caused by missing job deadlines.

Each job in the system has three main attributes: duration, deadline, and penalty cost if the job is completed after its deadline. The system evaluates different scheduling possibilities to find the sequence that results in the lowest total penalty.

To achieve this, the project implements two well-known search algorithms:

Uniform Cost Search

A* Search Algorithm

The algorithms explore possible job orders and calculate penalties to identify the most efficient schedule. In addition, the results are visualized using a Gantt Chart created with Matplotlib to clearly illustrate the execution timeline of each job.

This project demonstrates how AI search techniques can be applied to solve real-world scheduling and optimization problems.