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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Lagrangian Relaxation For Large-Scale Multi-Agent Planning, Geoffrey J. Gordon, Pradeep Varakantham, William Yeoh, Hoong Chuin Lau, Ajay S. Aravamudhan, Shih-Fen Cheng
Lagrangian Relaxation For Large-Scale Multi-Agent Planning, Geoffrey J. Gordon, Pradeep Varakantham, William Yeoh, Hoong Chuin Lau, Ajay S. Aravamudhan, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
Multi-agent planning is a well-studied problem with various applications including disaster rescue, urban transportation and logistics, both for autonomous agents and for decision support to humans. Due to computational constraints, existing research typically focuses on one of two scenarios: unstructured domains with many agents where we are content with heuristic solutions, or domains with small numbers of agents or special structure where we can provide provably near-optimal solutions. By contrast, in this paper, we focus on providing provably near-optimal solutions for domains with large numbers of agents, by exploiting a common domain-general property: if individual agents each have limited influence …
Lagrangian Relaxation For Large-Scale Multi-Agent Planning, Geoff Gordon, Pradeep Reddy Varakantham, William Yeoh, Ajay Srinivasan, Hoong Chuin Lau, Shih-Fen Cheng
Lagrangian Relaxation For Large-Scale Multi-Agent Planning, Geoff Gordon, Pradeep Reddy Varakantham, William Yeoh, Ajay Srinivasan, Hoong Chuin Lau, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
Multi-agent planning is a well-studied problem with applications in various areas. Due to computational constraints, existing research typically focuses either on unstructured domains with many agents, where we are content with heuristic solutions, or domains with small numbers of agents or special structure, where we can find provably near-optimal solutions. In contrast, here we focus on provably near-optimal solutions in domains with many agents, by exploiting influence limits. To that end, we make two key contributions: (a) an algorithm, based on Lagrangian relaxation and randomized rounding, for solving multi-agent planning problems represented as large mixed-integer programs; (b) a proof of …
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.
A Robust Unit Commitment Algorithm For Hydro-Thermal Optimization, Chao-An Li, Chung-Li Tseng, E. Hsu, R. B. Johnson, A. J. Svoboda
A Robust Unit Commitment Algorithm For Hydro-Thermal Optimization, Chao-An Li, Chung-Li Tseng, E. Hsu, R. B. Johnson, A. J. Svoboda
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. The authors 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.