Immune simulated annealing algorithm for the job shop scheduling problem

An immune simulated annealing algorithm based on bottleneck job identification was presented to solve the job shop scheduling problem with the objective of minimizing total weighted tardiness. Bottleneck characteristic values were defined to describe the criticality of each job on the final scheduling performance. Then, a fuzzy inference system was employed to evaluate the characteristic values by utilizing the human experience abstracted from practical scheduling environment. Finally, an immune mechanism was designed according to the bottleneck information and based on the idea that bottleneck jobs which can cause considerable deterioration to the overall performance measures should be considered with higher priority. Numerical computations were conducted with a simulated annealing algorithm which utilized the immune mechanism. Computational results for problems of different sizes show that the proposed algorithm achieves effective results by accelerating the convergence of the optimization process.