A new hybrid genetic algorithm for job shop scheduling problem

• Published in: Computers & operations research Vol. 39; no. 10; pp. 2291 - 2299
• Main Authors: Qing-dao-er-ji, Ren, Wang, Yuping
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.10.2012; Elsevier; Pergamon Press Inc
• Subjects: Algorithms; Applied sciences; Computer simulation; Convergence; Critical path; Crossover operator; Exact sciences and technology; Genetic algorithm; Genetic algorithms; Genetics; Job shop scheduling; Job shop scheduling problem; Job shops; Local search; Mutation operator; Operational research and scientific management; Operational research. Management science; Operators; Optimization algorithms; Production scheduling; Scheduling, sequencing; Searching; Studies; Mutation operator; Job shop scheduling problem; Local search; Genetic algorithm; Crossover operator; Human operator; Computer simulation; Job shop; Task scheduling; Machine operator; Scheduling; Concentration; Workload; NP hard problem; Critical path
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2011.12.005

Job shop scheduling problem is a typical NP-hard problem. To solve the job shop scheduling problem more effectively, some genetic operators were designed in this paper. In order to increase the diversity of the population, a mixed selection operator based on the fitness value and the concentration value was given. To make full use of the characteristics of the problem itself, new crossover operator based on the machine and mutation operator based on the critical path were specifically designed. To find the critical path, a new algorithm to find the critical path from schedule was presented. Furthermore, a local search operator was designed, which can improve the local search ability of GA greatly. Based on all these, a hybrid genetic algorithm was proposed and its convergence was proved. The computer simulations were made on a set of benchmark problems and the results demonstrated the effectiveness of the proposed algorithm.