.png){kind=icon}
.png){kind=icon}
Return
Novel mathematical formulations for parallel-batching processing machine scheduling problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cor.2024.106859.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We study mathematical formulations for batch-processing machine scheduling problems (BPMPs), which are the challenging issues in the machine scheduling literature where machines are capable of processing a batch of jobs simultaneously if jobs with non-identical sizes can be packed in a capacitated machine. In this paper, we tackle single- and parallel-machine BPMPs, and other interesting problem variants that aim at minimizing the makespan. We develop novel formulations along with valid inequalities and an algorithm framework that makes use of dual information and bounding techniques to achieve efficiency when instances are intractable. Extensive computational experiments on benchmark instances show that our approaches achieve state-of-the-art results and prove the optimality of intractable instances in the literature.
Keywords:
Scheduling
Batch-processing machines
Makespan
Valid inequalities
Mixed-integer linear programming
Journal
C
IF:
4.3
Papers:
6.5K
Citations:
1.8W