Comparing the impact of different rescheduling strategies on the entropic-related complexity of manufacturing systems
Comparing the impact of different rescheduling strategies on the entropic-related complexity of manufacturing systems
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比较不同的重新调度策略对制造系统与熵相关的复杂性的影响
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发表时间：
2009
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通讯作者：
Stella Kariuki
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作者：
L. Huaccho Huatuco;J. Efstathiou;A. Calinescu;S. Sivadasan;Stella Kariuki
本文的主要目的是比较五种重调度策略，根据其有效性，减少熵相关的复杂性所产生的机器故障在制造系统。熵相关复杂度是描述系统状态所需的预期信息量。作者之前进行的案例研究指导了计算机模拟，这些模拟是在竞技场5.0中结合MS Excel进行的。仿真性能的测量：（1）熵相关的复杂性措施，量化：（a）与时间表的信息内容相关的复杂性，（B）与时间表之间的变化相关的复杂性;和（2）平均流时间。结果突出了两个要点：（a）通过使用最少利用的机器来处理受机器故障影响的作业来减少不平衡的机器工作量的重要性，以及（B）低中断策略在降低熵相关复杂性方面是有效的;这意味着为了管理复杂性而应用重新调度策略在一定程度上是有益的，在低中断策略中，都包含在其阈值条件中。本文的贡献是双重的。首先，它扩展了熵相关的复杂性的应用程序，通过重新调度产生的每一个时间表，而以前的工作只适用于原来的时间表。第二，建议，以改善他们的重新安排的做法，在面对机器故障的制造商。这些建议根据制造组织的产品类型和调度目标而有所不同。进一步的工作包括：（a）准备一份详细的工作手册，以测量车间级的熵相关复杂性;以及（B）将分析扩展到其他类型的干扰，例如客户变更。
The primary objective of this paper is to compare five rescheduling strategies according to their effectiveness in reducing entropic-related complexity arising from machine breakdowns in manufacturing systems. Entropic-related complexity is the expected amount of information required to describe the state of the system. Previous case studies carried out by the authors have guided computer simulations, which were carried out in Arena 5.0 in combination with MS Excel. Simulation performance is measured by: (1) entropic-related complexity measures, which quantify: (a) the complexity associated with the information content of schedules, and (b) the complexity associated with the variations between schedules; and (2) mean flow time. The results highlight two main points: (a) the importance of reducing unbalanced machine workloads by using the least utilised machine to process the jobs affected by machine breakdowns, and (b) low disruption strategies are effective at reducing entropic-related complexity; this means that applying rescheduling strategies in order to manage complexity can be beneficial up to a point, which, in low disruption strategies, is included in their threshold conditions. The contribution of this paper is two-fold. First, it extends the application of entropic-related complexity to every schedule generated through rescheduling, whereas previous work only applied it to the original schedule. Second, recommendations are proposed to schedulers for improving their rescheduling practice in the face of machine breakdowns. Those recommendations vary according to the manufacturing organisations’ product type and scheduling objectives. Further work includes: (a) preparing a detailed workbook to measure entropic-related complexity at shop-floor level; and (b) extending the analysis to other types of disturbances, such as customer changes.