Improving automotive garage operations by categorical forecasts using a large number of variables
Improving automotive garage operations by categorical forecasts using a large number of variables
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DOI：
10.1016/j.ejor.2022.06.062
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Shixuan Wang;A. Syntetos;Ying Liu;C. Cairano-Gilfedder;M. Naim
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文献类型：
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作者：
Shixuan Wang;A. Syntetos;Ying Liu;C. Cairano-Gilfedder;M. Naim
车库管理的成本效益作业调度依赖于将维修时间分配到适当的类别，而不是使用确切的维修时间长度。在本文中，我们采用了有序logit模型与最小绝对收缩和选择算子（LASSO）预测汽车发动机的维修时间类别。我们的研究是基于一个独特的数据集的维护记录从网络的64英国车库ofBT车队解决方案，我们考虑了大量的预测变量，条件，制造，地理和维修相关的信息。LASSO的应用使得能够识别用于预测目的的相关预测变量。基于Brier分数和排名概率分数（以及它们的技能分数），我们记录了我们的方法的实质性预测能力，它优于五个基准，包括公司使用的方法。更重要的是，我们明确展示了如何将预测概率与损失函数相关联，以便在车库中做出运营决策。我们发现，最佳的选择作业调度并不总是对应于预测的类别，特别是当损失函数是不对称的。我们表明，调度工作的基础上，我们的方法可以帮助公司减少损失值。最后，我们确定了进一步改善公司运营和车库维护运营的机会。
Cost effective job scheduling for garage management relies upon assigning repair times into appropriate categories rather than using exact repair time lengths. In this paper, we employ an ordinal logit model with least absolute shrinkage and selection operator (LASSO) to forecast such repair time categories for automotive engines. Our study is based on a unique dataset of maintenance records from the network of 64 UK garages ofBT Fleet Solutions, and we consider a large number of predictor variables, with condition, manufacturing, geographical, and calendar-related information. The application of LASSO enables the identification of relevant predictor variables for forecasting purposes. Based on the Brier score and the ranked probability score (and their skill scores), we document substantial predictive ability of our method which outperforms five benchmarks, including the method used by the company. More importantly, we demonstrate explicitly how to associate the predicted probabilities with a loss function in order to make operational decisions in garages. We find that the best choice of job scheduling does not always correspond to the predicted categories, especially when the loss function is asymmetric. We show that scheduling jobs on the basis of our method can help the company reduce loss value. Finally, we identify opportunities for further improvements in the operations of the company and for garage maintenance operations in general.