A Multi-Machine Order Scheduling with Learning Using the Genetic Algorithm and Particle Swarm Optimization

The assembly of numerous applications can proceed only if all the parts for assembly are available. The completion time is determined largely by the time of manufacture of the final component. The setup times are included in the job processing time. It is unreasonable to assume that the setup process dominates the overall production process. Such activities are frequently encountered in process manufacturing, in which an initial setup is followed by a lengthy, uninterrupted production process. Motivated by these observations, we examine a multi-machine order scheduling problem with a sum-of-job-processing-times-based learning environment to minimize the number of tardy jobs. Dominance rules and a lower bound are first derived and applied in the branch-and-bound algorithm to identify the optimal solution. Afterward, a genetic algorithm and the particle swarm optimization method are employed to find a near-optimal solution. In addition, the experimental results of all proposed algorithms are provided.