A DECOMPOSITION-BASED OPTIMIZATION ALGORITHM FOR SCHEDULING LARGE-SCALE JOB SHOPS

A decomposition-based optimization algorithm is presented for large-scale job shop scheduling problems in which the total weighted tardiness should be minimized. The algorithm adopts an iterative optimization framework. In each iteration, a new subproblem is first defined by a simulated annealing approach and then solved using a particle swarm optimization algorithm. In order to promote the optimization efficiency, the jobs' bottleneck characteristic values are calculated and utilized as an immune mechanism to guide the subproblem-solving process. Numerical computations and comparisons are conducted for both randomly generated test problems and the real-life production environment of a speed-reducer factory in China. Experiment results reveal the unique advantages of the proposed algorithm over the existing methods.