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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >Metaheuristics to minimise makespan on parallel batch processing machines with dynamic job arrivals
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Metaheuristics to minimise makespan on parallel batch processing machines with dynamic job arrivals

机译：元启发法可最大程度地减少具有动态作业到达的并行批处理机器上的制造时间

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摘要

Batch processing machines that can process a group of jobs simultaneously are often encountered in semiconductor manufacturing and metal heat treatment. This research investigates the scheduling problem on parallel batch processing machines in the presence of dynamic job arrivals and non-identical job sizes. The processing time and ready time of a batch are equal to the largest processing time and release time among all jobs in the batch, respectively. This problem is NP-hard in the strong sense, and hence two lower bounds were proposed to evaluate the performance of approximation algorithms. An ERT-LPT heuristic rule was next presented to assign batches to parallel machines. Two metaheuristics, a genetic algorithm (GA) and an ant colony optimisation (ACO) are further proposed using ERT-LPT to minimise makespan. The performances of the two approaches, along with a BFLPTERTLPT (BE) heuristic were compared by computational experiments. The results show that both metaheurisitcs outperform BE. GA is able to obtain better solutions when dealing with small-job instances compared to ACO, whereas ACO dominates GA in large-job instances.
机译：在半导体制造和金属热处理中经常遇到可以同时处理一组作业的批处理机器。这项研究调查了在存在动态作业到达和作业大小不相同的情况下并行批处理机器上的调度问题。批处理的时间和就绪时间分别等于批处理中所有作业中最大的处理时间和发布时间。这个问题从强烈的意义上讲是NP难的，因此提出了两个下界来评估近似算法的性能。接下来提出了ERT-LPT启发式规则，以将批次分配给并行计算机。进一步提出了两种元启发法，一种是遗传算法（GA），另一种是蚁群优化（ACO）。通过计算实验比较了两种方法的性能以及BFLPTERTLPT（BE）启发式算法。结果表明，两种超敏性能均优于BE。与ACO相比，GA在处理小工作实例时能够获得更好的解决方案，而ACO在大工作实例中占主导地位。

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