Genetic Algorithm for Job Shop Scheduling Problem

This document presents a MATLAB implementation of a genetic algorithm designed to solve the Job Shop Scheduling Problem (JSSP). The JSSP represents a critical optimization challenge in manufacturing environments, focusing on optimal task arrangement within workshop settings to maximize production efficiency. The genetic algorithm approach enables identification of near-optimal scheduling solutions through evolutionary computation techniques. Key implementation features include: - Chromosome representation using operation-based encoding for scheduling sequences - Fitness function calculation based on makespan minimization - Genetic operators: tournament selection, partially matched crossover (PMXC), and swap mutation - Elite preservation strategy to maintain best solutions across generations The MATLAB source code provides a robust framework for JSSP optimization, incorporating adjustable parameters for population size, crossover rate, mutation probability, and termination conditions. The implementation supports customization for various workshop configurations and can handle multiple constraints including machine availability and operation precedence. This solution demonstrates practical value for both industrial applications and academic research, offering a foundation for further enhancements such as hybrid algorithms, multi-objective optimization, or real-time scheduling adaptations. Through systematic experimentation and code modification, users can adapt the algorithm to diverse JSSP variants and production scenarios.