Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems
Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems
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DOI：
10.1080/00207540701644219
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发表时间：
2009-04
影响因子：
9.2
通讯作者：
R. Zhou;A. Nee;H.P. Lee
中科院分区：
文献类型：
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作者：
R. Zhou;A. Nee;H.P. Lee
当前研究的目的是在动态车间调度问题的领域中确定适当的蚂蚁菌落优化（ACO）的应用域。该算法在商店地板方案中进行了三个级别的机器用途，三个不同的处理时间分布以及中间调度问题的三种不同性能指标的测试。将ACO的稳态性能在平均流动时间，平均迟发性，不同实验环境上的总吞吐量与派遣规则（包括首先出局，最短的处理时间和最小休息时间）进行了比较。进行了两组实验，以确定最佳的ACO策略和最佳性能调度规则。此后，这两种方法与处理时间的不同变化相比。实验结果表明，当机器利用率或处理时间变化不高时，ACO优于其他方法。
The goal of the current study is to identify appropriate application domains of Ant Colony Optimisation (ACO) in the area of dynamic job shop scheduling problem. The algorithm is tested in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems. The steady-state performances of ACO in terms of mean flow time, mean tardiness, total throughput on different experimental environments are compared with those from dispatching rules including first-in-first-out, shortest processing time, and minimum slack time. Two series of experiments are carried out to identify the best ACO strategy and the best performing dispatching rule. Those two approaches are thereafter compared with different variations of processing times. The experimental results show that ACO outperforms other approaches when the machine utilisation or the variation of processing times is not high.