Flow shop scheduling based on a novel cooling temperature driven simulated annealing algorithm

The Permutation Flow Shop Problem (PFSP) has been applied to many types of problems. The PFSP is an NP-hard permutation sequencing scheduling problem. A local search with simulated annealing scheme involving two phases is proposed in this investigation for solving PFSP. First, for lowering computation complexity, a simple insertion local search is applied to generate the solution of the PFSP. Second, two nondecreasing cooling temperature driven Simulated Annealing (SA), namely, steady SA and reheating SA are employed to maintain successive exploration or exploitation in the solution space. The steady SA maintains the same temperature and keeps the same search behavior and thereby allows the neighbors of the worse solutions to be explored, consequently increasing the chances of nding better solutions, while the reheating SA increases the temperature and exploration ability. The most important feature of the proposed method is its simple implementation and low computation time complexity. Experimental results are compared with other state-of-the-art algorithms and reveal that the proposed simple insertion with steady SA (SI-SSA) method is able to eciently yield the best permutation schedules.