Abstract
We study Pareto-scheduling on an unbounded parallel-batch machine that can process any number of jobs simultaneously in a batch. The processing time of a batch is equal to the maximum processing time of the jobs in the batch. We consider two Pareto-scheduling problems. In one problem, the jobs are partitioned into families and the jobs from different families cannot be processed together in the same batch. We assume that the number of families is a constant. The objective is to minimize the makespan and the maximum cost. In the other problem, we have two agents A and B, where each agent \(E\in \{A,B\}\) has its job set \({{\mathcal {J}}}_{E}\), called the E-jobs. Assuming that the job sets \({{\mathcal {J}}}_{A}\) and \({{\mathcal {J}}}_{B}\) are not necessarily disjoint, we call the agents \(\text {ND}\) agents. The objective is to minimize the makespan of the A-jobs and the maximum cost of the B-jobs. We provide polynomial-time algorithms to solve the two Pareto-scheduling problems.
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Acknowledgements
The authors would like to thank the Associate Editor and two anonymous referees for their constructive comments and helpful suggestions. Gao was supported by the China Postdoctoral Science Foundation under Grant Number 2019M652555 and the National Natural Science Foundation of China under Grant Number 11901539. Yuan was supported by the National Natural Science Foundation of China under Grant Numbers 12071442 and 11771406.
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Gao, Y., Yuan, J., Ng, C.T. et al. Pareto-scheduling with family jobs or ND-agent on a parallel-batch machine to minimize the makespan and maximum cost. 4OR-Q J Oper Res 20, 273–287 (2022). https://doi.org/10.1007/s10288-021-00480-3
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DOI: https://doi.org/10.1007/s10288-021-00480-3