Ant colony optimization with local search applied to the Flexible Job Shop Scheduling Problems

Flexible job-shop scheduling problem known as FJSSP is a NP-hard problem which attracts great attentions from researchers for decades. In this paper, a new approach of ant colony optimization with local search (ACOLS) is presented to solve the FJSSP. In the ACOLS, a new heuristic information is designed to balance the workloads between machines while ants tend to select the machine with less processing time for those operations. SPT scheduling rule is used to sequence the operations on each machine. In each iteration, a designed local search is used to search the neighborhood of the optimal solution obtained in each iteration for possible better solutions by the criterions of less total workloads and their variance for all machines. Simulation results show that the proposed ACOLS is very efficient compared with the basic ACO and other algorithms to deal with FJSSP.