Research on Unrelated Parallel Machine Scheduling Problem Considering Machine Eligibility

Consider an unrelated parallel machine scheduling problems with machine eligibility, where each processed workpiece can only be arranged on specific machines for processing. In this paper, we propose a particle swarm optimization (PSO) algorithm which introduces an elite reverse learning strategy and sine and cosine optimization algorithm to minimize the maximum completion time. In this algorithm, the elite reverse learning strategy is introduced heuristically to provide better feasible solutions for the algorithm randomization, thus accelerating the convergence rate. In addition, the sine and cosine optimization algorithm is introduced in the particle iteration process to update the velocity vector of the particle swarm, so as to enhance the diversity of the population. The effectiveness and advantage of the new PSO in solving the problem are verified by simulation experiments and comparation with other algorithms.