Dynamic scheduling of Diffusion Furnace in Semiconductor Manufacturing with real time events

This study addresses a scheduling problem observed in the Diffusion Furnace (DF) of Semiconductor Manufacturing industry. Most of the earlier research in dynamic scheduling of DF(a Batch Processing Machine) considers only dynamic arrival of jobs. This study focuses dynamic scheduling of DF considering future arrival of jobs along with job and resource related real time events to minimize the total weighted tardiness. In the literature there are some studies addressing the dynamic real-time scheduling for discrete machine environment. These studies are concentrating either on proposing new algorithms and/or fine tuning the existing algorithms due to the occurrence of real-time events while scheduling. In this study we propose a research hypothesis that no needs to change any existing efficient dynamic scheduling algorithm(s) while real time events are occurring in scheduling DF. From the series of computational experiments, this study proves the proposed research hypothesis both empirically and statistically on nine efficient variants of ATC/BATC based greedy heuristic algorithms.