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A Parallel Genetic-Neuro Scheduler For Job-Shop Scheduling Problems, H. C. Lee, Cihan H. Dagli
A Parallel Genetic-Neuro Scheduler For Job-Shop Scheduling Problems, H. C. Lee, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
Despite relentless efforts on developing new approaches, there are still large gaps between schedules generated through various planning systems, and schedules actually used in the shop floor environment. An effective schedule generation is a knowledge intensive activity requiring a comprehensive model of a factory and its environment at all times. There are four main difficulties that need to be addressed. First, job shop scheduling belongs to a class of NP-hard problems. Second, it is a highly constrained problem that changes from shop to shop. Third, scheduling decisions depend upon other decisions which are not isolated from other functions. Thus, it …
Short-Term Resource Scheduling With Ramp Constraints [Power Generation Scheduling], Chung-Li Tseng, Chao-An Li, A. J. Svoboda, R. B. Johnson
Short-Term Resource Scheduling With Ramp Constraints [Power Generation Scheduling], Chung-Li Tseng, Chao-An Li, A. J. Svoboda, R. B. Johnson
Engineering Management and Systems Engineering Faculty Research & Creative Works
This paper describes a Lagrangian relaxation-based method to solve the short-term resource scheduling (STRS) problem with ramp constraints. Instead of discretizing the generation levels, the ramp rate constraints are relaxed with the system demand constraints using Lagrange multipliers. Three kinds of ramp constraints, startup, operating and shutdown ramp constraints are considered. The proposed method has been applied to solve the hydro-thermal generation scheduling problem at PG&E. An example alone with numerical results is also presented