An essential task in manufacturing planning and control is to determine when to release orders to the shop floor. One key parameter is the lead time which is the planned time that elapses between the release of an order and its completion. Lead times are normally determined based on the observed time orders previously took to traverse the production system (flow times). Traditional order release models assume static lead times, although it has been shown that they should be set dynamically to reflect the dynamics of the system. Therefore, we present a flow time estimation procedure to set lead times dynamically using an artificial neural network. Additionally, we implement a safety lead time to incorporate the underlying cost ratio between finished inventory holding and backorder costs in the order release model. We test our proposed approach using a simulation model of a three-stage make-to-order flow-shop and compare the forecast accuracy and the cost performance to other forecast-based order release models from the literature. We show that our proposed model using artificial neural networks outperforms the other tested approaches, especially for scenarios with high utilisation and high variability in processing times.

制造计划和控制中的一项基本任务是确定何时向车间下达订单。一个关键参数是提前期，即从发布订单到完成订单之间经过的计划时间。提前期通常是根据观察到的先前遍历生产系统所花费的时间订单（流程时间）来确定的。传统的订单发布模型假设静态提前期，尽管已经表明它们应该动态设置以反映系统的动态。因此，我们提出了一个流程时间估计程序，以使用人工神经网络动态设置提前期。此外，我们实施了安全提前期，以在订单发布模型中纳入成品库存持有成本和延期交货成本之间的基本成本比率。我们使用三阶段按订单生产流水车间的模拟模型测试我们提出的方法，并将预测准确性和成本绩效与文献中的其他基于预测的订单发布模型进行比较。我们表明，我们提出的使用人工神经网络的模型优于其他经过测试的方法，尤其是在处理时间具有高利用率和高可变性的情况下。