Parallel-batching machines scheduling problem with a truncated time-dependent learning effect via a hybrid CS-JADE algorithm

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This research investigates the parallel-batching scheduling problems with a time-dependent learning effect where the job processing time is a decreasing function of its starting time. Both the single-machine and parallel-machine circumstances are considered, and the objective is to minimize the makespan. For the single parallel-batching machine scheduling problem, some structural properties and a heuristic algorithm are developed to solve it. Since the parallel-batching machines scheduling problem is NP-hard, a hybrid CS-JADE algorithm combining improved cuckoo search algorithm (CS) and self-adaptive differential evolution (DE) is proposed to solve this problem. The computational experiments indicate that the proposed hybrid algorithm performs well both in effectiveness and efficiency.