An ant colony algorithm for job shop scheduling problem with tool flow

In this article, we present a developed bidirectional convergence ant colony algorithm to solve the integrated job shop scheduling problem with tool flow in flexible manufacturing system. In particular, the optimization problem for a real environment, including system make-span and waiting time for tools, has been approached by means of an effective pheromone trail coding and tailored ant colony operators for improving solution quality. The algorithm provides an effective integration between operation sequence and tool selection. A new principle of state transition probability is proposed with consideration of the waiting time for tools, and an optimization method of tool assignment is put forward. The proposed algorithm employs a machine decomposition method inspired by operations that are processed on fixed machines. The ant just gives the partial solution on one machine each time to construct the global scheduling solution with the previous solution on the other machines. This method performs well using the efficiency of ant colony algorithm for solving job shop scheduling problem. The proposed algorithm is tested by a series of simulation experiments, and interpretations of the results are also presented. Final experimental results indicate that the developed bidirectional convergence ant colony algorithm outperforms some current approaches in job shop scheduling problem with tool flow.