Genetic Algorithm Program for Workshop Job Scheduling

This is a MATLAB-based genetic algorithm program designed for workshop job scheduling optimization. The program implements key genetic algorithm components including chromosome encoding (typically using permutation-based representation for job sequences), fitness evaluation (calculating makespan or total completion time), selection operations (roulette wheel or tournament selection), crossover (such as PMX or OX operators for preserving job sequences), and mutation mechanisms (swap or inversion mutations). This implementation helps optimize workshop job scheduling to enhance production efficiency and resource utilization. Through genetic algorithm optimization, better job scheduling solutions can be obtained, enabling more rational assignment of jobs to machines and workers, thereby reducing job waiting times and completion times. The program features customizable parameters including population size, generations, crossover/mutation rates, and can be adapted to different workshop layouts and job requirements. Users can modify the initialization function to match specific machine configurations and job characteristics, while the objective function can be extended to incorporate various constraints like due dates or resource limitations. This flexible implementation allows adaptation to diverse production environments and requirements. By utilizing this program, you can effectively manage and arrange workshop operations, improve production efficiency, and reduce operational costs. The code structure follows modular design with separate functions for initialization, fitness calculation, genetic operations, and result visualization, making it suitable for both educational and industrial applications. This program aims to assist in achieving optimal workshop scheduling and management solutions.