Hybrid Ant Colony Multi-Objective Optimization for Flexible Job Shop Scheduling Problems

Abstract

In this paper, considering multiple objectives, a Hybrid Ant Colony Optimization (HACO) is proposed to deal with the Flexible Job-Shop Scheduling Problem (FJSSP). In the HACO, ant colony optimization is used to assign operations to machines, where a new combined heuristic is designed to balance the workloads between machines while ants tend to select the machine with less processing time for those operations. After that, SPT scheduling rule is applied to sequence the operations on each machine to shorten the makespan as well as to meet the desired delivery time window. To improve the globe search performance, a designed local search is used to search the neighborhood of an obtained optimal solution for possible better solutions by the criterions of less total workloads and their variance for all machines. Simulation results show that the proposed HACO is very efficient compared with the basic ACO and other algorithms in dealing with FJSSPs.