Abstract

Most production environments are stochastic in nature, due to the randomness inherent in the production processes. One important engineering problem commonly faced by practitioners is to determine optimal engineering tolerances to be used in production. This article develops optimization models for determining tolerance sets to maximize the long-run average net profit on a production line with processing and rework stations, as well as instantaneous inspection and scrap operations. We assume that only one server works at the rework station, and the service times at the processing and rework stations are uncertain, thus, a stochastic queueing system is embedded into the manufacturing process. We also consider the trade-off between the overall production cost and the cost associated with a quality loss in the final product. Our work is the first to introduce the concept of double-tolerance sets to the tolerance design optimization literature. By comparing the proposed double-tolerance model with a single-tolerance model, we investigate the impact of different parameter settings and modeling assumptions on the optimal tolerances through numerical examples and a sensitivity analysis.

摘要

由于生产过程中固有的随机性，大多数生产环境本质上是随机的。从业者通常面临的一个重要工程问题是确定要在生产中使用的最佳工程公差。本文开发了用于确定公差集的优化模型，以最大化具有加工和返修站以及即时检查和报废操作的生产线的长期平均净利润。我们假设返修台只有一台服务器，加工和返修台的服务时间是不确定的，因此在制造过程中嵌入了随机排队系统。我们还考虑了总生产成本和与最终产品质量损失相关的成本之间的权衡。我们的工作是第一个将双公差集的概念引入公差设计优化文献的工作。通过比较所提出的双公差模型和单公差模型，我们通过数值例子和敏感性分析研究了不同参数设置和建模假设对最佳公差的影响。