Job Shop Scheduling Problem Genetic Algorithm Implementation

In this document, I would like to share a general-purpose genetic algorithm program designed for solving job shop scheduling problems. Genetic algorithms are heuristic optimization methods that have been widely applied to various problems, including job shop scheduling challenges. By implementing this genetic algorithm, we can identify optimal scheduling schemes that maximize production efficiency and resource utilization through evolutionary computation.

The genetic algorithm operates by simulating natural evolutionary processes. It begins by initializing a population consisting of multiple individuals, where each individual represents a potential schedule solution. The algorithm then implements three core genetic operators: selection (based on fitness scores), crossover (combining parent solutions), and mutation (introducing random variations). These operations iterate across generations, progressively improving population quality and converging toward near-optimal solutions through fitness-driven evolution.

To utilize this general-purpose program, users need to provide essential job shop scheduling parameters including the number of jobs, machine count, and process relationships between operations. The program automatically processes these inputs through its evolutionary framework to generate optimized scheduling solutions. Key customizable parameters include population size, crossover rate (typically 0.6-0.9), mutation rate (usually 0.01-0.1), and termination criteria (generation count or convergence threshold), allowing adaptation to specific operational requirements.

Please examine the attached program files and modify the configuration according to your specific scenario. The implementation includes modular components for fitness calculation, chromosome encoding/decoding, and genetic operator implementation. Should you have any technical inquiries or require implementation assistance, please feel free to contact me. Thank you for your interest in this optimization solution!