Parallel IG algorithm for matrix shop AGV scheduling problem with time and capacity constraints

In the current research on scheduling problems, intelligent optimization algorithms have been widely applied by scholars as excellent solutions. The advantage of this method is that it can obtain better solutions for NP-hard problems in a short period of time. The limitation of current optimization methods is that they cannot obtain larger computational space in a short period of time. This paper proposes a parallel iterative greed algorithm (PIGA) that combines intelligent optimization algorithms with parallel computing to achieve larger computational space. In the proposed PIGA, four operators and a restart strategy are used to enhance the algorithm's global and local search capabilities. To verify the performance of the proposed algorithm, PIGA was applied to the matrix workshop automated guided vehicle (AGV) scheduling problem and compared with other optimization algorithms. The experimental results demonstrate the superiority of the proposed algorithm.