A novel Ant Colony Optimization (ACO) algorithm, ACO-BAT, was presented for the No-Wait flow-line batching and scheduling problem, where the jobs are partitioned into groups, jobs of the same group can be processed simultaneously as a batch by the batch processing machines, but with limited batch size. The batch-sequence-dependent setup time of the batch processing machines, and the batch transfer time are considered in the problem. In the ACO-BAT algorithm, the artificial ants iteratively construct feasible job batching and batch sequencing solutions, guided by the pheromone distributed in the solution space. Comparisons with other algorithms on the extended Taillard's benchmark problems show that our algorithm is very efficient and robust.
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