Effective hybrid procedures based on evolutionary algorithms and simulated annealing algorithms for job shop scheduling problems

Four effective hybrid procedures are proposed for the job shop scheduling problems by combining evolutionary algorithms (EA) with simulated annealing algorithms (SA). These are genetic-simulated annealing algorithms (GSA), enhanced genetic algorithms (EGA), enhanced evolutionary programming (EEP) and parallel simulated annealing algorithms (PSA). The cooperation of EA and SA intensify the neighborhood search and to avoid premature convergence. The neighborhood search template that employs a critical path is adopted to decrease the search area and improve the efficiency of the exploration. Numerical simulation demonstrates that within the framework of the newly designed hybrid algorithms, the NP-hard classic job-shop scheduling problem can be efficiently solved with higher quality, and that the optimization performances of hybrid procedures are superior to the algorithm reported in the literature. The simulation also indicates that the search ability of mutations based on SA is stronger than crossover operation and that the optimization power of EEP is better than other hybrid procedures.