Abstract

We consider randomly failing high-precision machine tools in a discrete manufacturing setting. Before a tool fails, it goes through a defective phase where it can continue processing new products. However, the products processed by a defective tool do not necessarily generate the same reward obtained from the ones processed by a normal tool. The defective phase of the tool is not visible and can only be detected by a costly inspection. The tool can be retired from production to avoid a tool failure and save its salvage value; however, doing so too early causes not fully using the production potential of the tool. We build a Markov decision model and study when it is the right moment to inspect or retire a tool with the objective of maximizing the total expected reward obtained from an individual tool. The structure of the optimal policy is characterized. The implementation of our model by using the real-world maintenance logs at the Philips shaver factory shows that the value of the optimal policy can be substantial compared to the policy currently used in practice.

摘要

我们考虑在离散的制造环境中随机破坏高精度机床。在工具出现故障之前，它会经历有缺陷的阶段，在该阶段中，它可以继续处理新产品。但是，用有缺陷的工具加工的产品不一定能获得与用常规工具加工的产品相同的报酬。工具的缺陷阶段是不可见的，只能通过昂贵的检查才能发现。该工具可以退出生产，以避免工具故障并节省其残值；但是，这样做太早会导致无法充分利用工具的生产潜力。我们建立一个马尔可夫决策模型，并研究何时是检查或退役工具的恰当时机，目的是使从单个工具获得的总预期收益最大化。最优策略的结构被表征。通过使用飞利浦剃须刀工厂的实际维护日志来执行我们的模型，结果表明，与当前实际使用的策略相比，最佳策略的价值可观。