Solving Job Shop Scheduling Problem with Genetic Algorithm

The method for solving Job Shop Scheduling Problems (JSSP) is critically important as it helps enterprises address time management challenges in production processes. As one of the most classic NP-hard problems in operational research, JSSP requires specialized knowledge and technical skills for effective resolution. This implementation provides detailed model explanations covering chromosome encoding, fitness function design, and genetic operators (selection, crossover, mutation). The Gantt chart generation module demonstrates how to visualize scheduling results using MATLAB's plotting functions, including timeline arrangement and machine allocation displays. The program structure follows object-oriented principles with separate modules for genetic algorithm operations, schedule evaluation, and result visualization. Key functions include population initialization with permutation-based encoding, tournament selection mechanisms, and specialized crossover operators like PMX (Partially Mapped Crossover) tailored for JSSP constraints. The executable program runs through the main file with configurable parameters for population size, iteration count, and mutation rates, enabling enterprises to optimize production timelines and enhance operational efficiency.