A Flexible Job-shop Scheduling Method Based on Hybrid Genetic Annealing Algorithm

摘要

In terms of characteristics of flexible job-shop scheduling problem, the complementary constraint models, including priority among different jobs and parent-child relations of relevant jobs, are established based on two objective function of minimizing makespan for all jobs and the sum of standard deviation of processing workload for all working centers. Aiming at the above model, a hybrid genetic annealing algorithm is proposed. The flow and key technologies of algorithm, including chromosome encoding and decoding scheme, fitness function, selection operation, self-adaptive crossover and mutation operation, neighborhood structure design and acceptance criterion, cooling function, algorithm termination condition and parallel computation, are discussed in detail. The feasibility and validity of the proposed model and algorithm is demonstrated through computational experiment.