PGGA: A predictable and grouped genetic algorithm for job scheduling

This paper presents a predictable and grouped genetic algorithm (PGGA) for job scheduling. The novelty of the PGGA is twofold: (1) a job workload estimation algorithm is designed to estimate a job workload based on its historical execution records, (2) the divisible load theory (DLT) is applied to predict an optimal solution in searching a large scheduling space so that the convergence process can be speeded up. Comparison with traditional scheduling methods such as first‐come‐first‐serve (FCFS), random scheduling and a typical genetic algorithm (TGA) indicates that the PGGA is more effective and efficient in finding optimal scheduling solutions.