Scheduling a single batch-processing machine with non-identical job sizes in fuzzy environment using an improved ant colony optimization

In this paper, we introduce the fuzzy model of the makespan on a single batch-processing machine with non-identical job sizes. The uncertainty of the jobs and the machine in the processing is denoted using fuzzy logic. Then an improved ant colony optimization (ACO) method is proposed and the Metropolis criterion is used to select the paths of ants to overcome the immature convergence of the algorithm. In the experiment, we adopt the random instances and the results of the fuzzy makespan demonstrate that the proposed algorithm outperforms GA and SA in all instances.