Short-term supervisory control of manufacturing systems.

Abstract

Bottlenecks within a production line significantly reduce the productivity. Quick and correct identification of the bottleneck locations can lead to an improvement in the operations management of utilizing finite manufacturing resources, increasing the system throughput, and minimizing the total cost of production. Most of the current bottleneck detection schemes focus on the long-term bottleneck detection problem and an analytical or simulation model is usually needed. Due to the recent developments, short-term process control and quick decision making on the plant floor have emerged as important research aspects for operations management. This dissertation proposes a new data driven method for throughput bottleneck detection in both the short and long term without building an analytical or simulation model. Furthermore, a supervisory control strategy is constructed to make decisions on efficiently improving system performance towards balanced-line status by considering the bottleneck detection results, maintenance opportunities and equipment reliability estimation. The method has been verified both analytically and by simulation. Industrial case studies have also been used in order to demonstrate the implementation and validate the efficiency of the proposed control strategy.