Dynamic Electric Power Supply Chains and Transportation Networks: An Evolutionary Variational Inequality Formulation
In this paper, the authors develop a static electric power supply chain network equilibrium model with known demands and establish the equivalence between the model and a transportation network equilibrium model with fixed demands over an appropriately constructed supernetwork. This equivalence yields a new interpretation of electric power supply chain network equilibria in path flows. The authors then exploit this equivalence to propose a dynamic electric power supply chain network model in which the demand varies over time using an evolutionary variational inequality formulation. Finally, the authors demonstrate how numerical dynamic electric power supply chain network problems can be solved utilizing recently obtained theoretical results in the unification of evolutionary variational inequalities and projected dynamical systems.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13665545
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Supplemental Notes:
- Abstract reprinted with permission from Elsevier
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Authors:
- Nagurney, Anna
- Liu, Zugang
- Cojocaru, Monica-Gabriela
- Daniele, Patrizia
- Publication Date: 2007-9
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References; Tables;
- Pagination: pp 624-646
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Serial:
- Transportation Research Part E: Logistics and Transportation Review
- Volume: 43
- Issue Number: 5
- Publisher: Elsevier
- ISSN: 1366-5545
- Serial URL: http://www.sciencedirect.com/science/journal/13665545
Subject/Index Terms
- TRT Terms: Demand; Dynamic models; Electric power supply; Networks; Numerical analysis; Supply chain management; Theoretical studies; Theory; Traffic control; Traffic equilibrium; Transportation; Variational inequalities (Mathematics)
- Subject Areas: Operations and Traffic Management; Transportation (General); I73: Traffic Control;
Filing Info
- Accession Number: 01055953
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 30 2007 2:10PM