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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
A Transmission-Constrained Unit Commitment Method, Chung-Li Tseng, Chao-An Li, S. S. Oren, C. S. Cheng, A. J. Svoboda, R. B. Johnson
A Transmission-Constrained Unit Commitment Method, Chung-Li Tseng, Chao-An Li, S. S. Oren, C. S. Cheng, A. J. Svoboda, R. B. Johnson
Engineering Management and Systems Engineering Faculty Research & Creative Works
The paper presents a transmission-constrained unit commitment method using a Lagrangian relaxation approach. The transmission constraints are modeled as linear constraints based on a DC power flow model. The transmission constraints, as well as the demand and spinning reserve constraints, are relaxed by attaching Lagrange multipliers. The authors take a new approach in the algorithmic scheme. A three-phase algorithm is devised including dual optimization, a feasibility phase and unit decommitment. A test problem involving more than 2500 transmission lines and 2200 buses is tested along with other test problems
A Robust Unit Commitment Algorithm For Hydro-Thermal Optimization, Chao-An Li, R. B. Johnson, A. J. Svoboda, Chung-Li Tseng, E. Hsu
A Robust Unit Commitment Algorithm For Hydro-Thermal Optimization, Chao-An Li, R. B. Johnson, A. J. Svoboda, Chung-Li Tseng, E. Hsu
Engineering Management and Systems Engineering Faculty Research & Creative Works
This paper presents a unit commitment algorithm which combines the Lagrangian relaxation (LR), sequential unit commitment (SUC), and optimal unit decommitment (UD) methods to solve a general hydro-thermal optimization (HTO) problem. We argue that this approach retains the advantages of the LR method while addressing the method''s observed weaknesses to improve overall algorithm performance and quality of solution. The proposed approach has been implemented in a version of PG&E''s HTO program, and test results are presented.
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
Application Of Distributed Knowledge Bases In Intelligent Manufacturing, Cihan H. Dagli, Gerald E. Hoffman
Application Of Distributed Knowledge Bases In Intelligent Manufacturing, Cihan H. Dagli, Gerald E. Hoffman
Engineering Management and Systems Engineering Faculty Research & Creative Works
The authors consider independent knowledge bases operating on separate work stations networked together within the domain of knowledge-based scheduling. The scheduling problem is addressed through distributed knowledge bases that have an ability to pass information back and forth between small knowledge bases functioning at different decision-making levels. A small manufacturing plant is conceptualized in order to experiment with this process. The general outline and areas of the manufacturing shop are shown. The domain specific area for the knowledge bases is to optimize the scheduling of work at each work station in order to meet a weekly quota. Automatic guided vehicle, …
Possible Applications Of Neural Networks In Manufacturing, S. Lammers, Cihan H. Dagli
Possible Applications Of Neural Networks In Manufacturing, S. Lammers, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
Summary form only given. An examination is made of the potential of neural networks and the impact of parallel processing in the design and operations of manufacturing systems. After an initial discussion on possible areas of application, an approach that integrates artificial intelligence, operations research, and neural networks for the solution of a scheduling problem is examined