Integrating production control and scheduling based on real-time detection of divergence

Scheduling and process control have been long recognized as the two critical building blocks in many manufacturing execution ystems. Operating at the interface between the supply chain and the process, the scheduler generates a detailed schedule that has to be executed by the process so as to meet the demands originating from the supply chain. Given the tight interactions between the two, there has been wide interest in integrating scheduling and process control. Our key insight is that abnormalities which occur post the generation of the original schedule, trigger a divergence between operational targets defined by the schedule and its execution. If left uncorrected, the abnormalities will propagate between the process and the supply chain. A timely response could eliminate or minimize such effects. Recognizing this, we propose a novel framework for integrating scheduling and process control that detects in real-time when a divergence occurs between the original schedule and its execution in the process, identifies the root-cause(s) of the divergence (i.e. the abnormality), and triggers a suitable response from the scheduler and the process so as to nullify or minimize its effect. In this thesis, we will describe the proposed approach and illustrate it using an industrially motivated case study.