An Adaptive Repulsive Particle Swarm Optimization for Makespan and Maximum Lateness Minimization in the Permutation Flowshop Scheduling Problem

This paper proposes an adaptive repulsive particle swarm optimization (ARPSO) for minimizing the makespan and maximum lateness in the permutation flowshop scheduling problem (PFSP). ARPSO develops a heuristic rule called the smallest distance value (SDV) to present the discrete job permutation for the PFSP. And ARPSO uses several novel evolutionary strategies to avoid premature convergence and improve its continuous optimization ability. Those strategies include adaptive repulsion technique and adaptive non-linearly varying acceleration coefficients. The results show that ARPSO outperforms its competitors.