A computational study of local search algorithms for job shop scheduling

We present a computational performance analysis of local search algorithms for job shop scheduling. The algorithms under investigation are iterative improvement, simulated annealing, threshold accepting, and genetic local search. Our study shows that simulated annealing performs best in the sense that if finds better solutions than the other algorithms within the same amount of running time. Compared to more tailored algorithms, simulated annealing still finds the best results but only under the assumption that running time is of no concern. Compared to tabu search, simulated annealing is outperformed especially with respect to running times.