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Risk cost estimation of job shop scheduling with random machine breakdowns

机译：随机机故障的作业商店调度风险成本估算

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摘要

Scheduling has been playing an important role in the manufacturing phase of product life cycle. In this paper, we focus on the estimation of risk cost for the job shop scheduling under random machine breakdowns, in which all jobs should be delivered together at a given due date. The risk cost measures the sum of expected earliness and tardiness costs. Considering that the risk cost in the form of expectation does not allow analytical calculation for the job shop scheduling, we will try to build a computable analytical approximation to replace the commonly used but time-consuming Monte Carlo simulation. However, the manual design of an effective analytical approximation is generally very complicated. To address it, we will develop a learning method based on the symbolic regression to extract an analytical approximation of risk cost from experimental data automatically. For this purpose, we first list all the features which may be related to the risk cost by analyzing deeply the job shop scheduling under random machine breakdowns. Then, a learning algorithm based on the genetic programming is proposed to extract an analytical approximation of risk cost. Finally, extensive experiments have shown that the accuracy of the generated analytical approximation in evaluating the risk cost is close to that of the Monte Carlo simulation, while it can significantly improve the efficiency of estimation.
机译：调度一直在产品生命周期的制造阶段发挥着重要作用。在本文中，我们专注于随机机器故障下的作业商店调度风险成本的估算，其中所有工作都应在给定的截止日期一起在一起。风险成本测量预期的预期和迟到费用。考虑到预期形式的风险成本不允许对作业商店调度的分析计算，我们将尝试构建可计算的分析近似，以替换常用但耗时的蒙特卡罗模拟。然而，有效分析近似的手动设计通常非常复杂。为了解决它，我们将基于符号回归开发一种学习方法，以自动从实验数据中提取风险成本的分析近似。为此目的，我们首先列出了通过在随机机器故障下的作业商店调度来与风险成本相关的所有功能。然后，提出了一种基于遗传编程的学习算法，提取风险成本的分析近似。最后，广泛的实验表明，在评估风险成本时产生的分析近似的准确性接近蒙特卡罗模拟的准确性，而它可以显着提高估计效率。

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