Abstract
This research examines production control problems in two-station serial production systems under process queue time (PQT) constraints. In these serial production systems, all jobs must be processed at a fixed order in the upstream and then downstream stations. There are multiple machines in both stations, and all machines are subject to random machine failures. In the downstream queue, the sum of waiting and processing time for each job is limited by an upper bound. This upper bound of time is called the PQT constraint. Violation of the PQT constraint causes high rework or scrap costs. An important application of this research is the control of semiconductor fabrication processes. In advanced (20, 28, and 40 nanometer technology node) 300mm semiconductor manufacturing systems, 60% of the manufacturing steps are PQT related. Any violation of PQT constraints seriously impacts yield quality and incurs significant scrap costs. In addition to semiconductor manufacturing, PQT constraints are common in other industries, including Thin Film Transistor Liquid Crystal Display (TFT-LCD), food processing production and steel production. In this research, an admission control model is formulated by Markov decision processes (MDP). In practice, job arrivals are random and unknown in advance. Moreover, servers and machines are not reliable and subject to failures and maintenance. Because real time reliability status of all machines is explicitly considered, computational efficiency suffers from the well-know “curse of dimensionality” of dynamic programming. To overcome the computational complexity issue, we prove the existence of optimal exhaustive production control policy. Based on the existence of optimal exhaustive control policies, an efficient algorithm is designed to significantly reduce computational time. Compare with other control methods in literature, significant performance improvement is observed in our simulation study.