AI-Based Job Scheduling Optimization System

Job Scheduling is a classical optimization problem where multiple jobs, each consisting of a sequence of operations, must be scheduled on a limited number of machines. Each operation must follow a strict order within its job, while machines can process different operations in parallel as long as resource and capacity constraints are respected.

The main objective of the scheduling process is to minimize the makespan, which represents the total time required to complete all jobs.

This project implements an AI-based Job Scheduling System that solves and analyzes the scheduling problem using two different algorithms:

• Backtracking Algorithm – an exact search method that explores all feasible schedules to find the optimal solution. This approach guarantees optimal results but is computationally expensive and practical only for small problem instances (effective for approximately ≤ 4 jobs).

• Cultural Algorithm – an evolutionary metaheuristic optimization technique that uses population-based search combined with a belief space to guide the search toward better solutions. This method scales better for larger scheduling problems.

The system also includes a Graphical User Interface (GUI) that allows users to interactively configure and analyze scheduling scenarios.

System Features

• Configure the number of jobs and their operations

• Define machine capacities and processing constraints

• Execute both scheduling algorithms

• Visualize generated schedules using Gantt charts

• Compare algorithm performance and scheduling results quantitatively

Technologies Used

Python

Artificial Intelligence Algorithms

Backtracking Search

Cultural Algorithm

Tkinter (GUI)

Matplotlib (Visualization)