Resilient design of recharging station networks for electric transportation vehicles

Villez, Kris; Gupta, Akshya; Venkatasubramanian, Venkat; Rieger, Craig

doi:10.1109/isrcs.2011.6016089

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…A related application area that have used the rational choice paradigm for modeling agents, is multi-agent networks. These applications study complex networks, by combining network science, rational choice theory and other related areas like evolutionary algorithms, to study different kinds of emergent properties arising from agents acting rationally in a networked environment Mei et al (2015); Patil, Srinivas, et al (2009);Patil, Srinivasa, & Venkatasubramanian (2009); Villez et al (2011). Some indicative problems addressed by multi-agent networks include: constrained negotiation and agreement protocols (Nedic et al, 2010;Meng & Chen, 2013), modeling diffusion and synchrony Faber et al (2010); Kiesling et al (2012); Kim et al (2011), etc.…”

Section: Rational Choice Based Modelsmentioning

confidence: 99%

The Evolution of Computational Agency

Srinivasa

Deshmukh

2020

Novel Approaches to Information Systems Design

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Agent-based models have emerged as a promising paradigm for addressing ever increasing complexity of information systems. In its initial days in the 1990s when object-oriented modeling was at its peak, an agent was treated as a special kind of “object” that had a persistent state and its own thread of execution. But since then, agent-based models have diversified enormously to even open new conceptual insights about the nature of systems in general. This chapter presents a perspective on the disparate ways in which our understanding of agency, as well as computational models of agency, have evolved. Advances in hardware like GPUs that brought neural networks back to life may also similarly infuse new life into agent-based models and pave the way for significant advancements in research on artificial general intelligence (AGI).

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Section: Rational Choice Based Modelsmentioning

confidence: 99%

The Evolution of Computational Agency

Srinivasa

Deshmukh

2020

Novel Approaches to Information Systems Design

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Multi-objective optimal path selection in electric vehicles

Siddiqi

Shiraishi

Sait

2012

Artif Life Robotics

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Data quality analysis and improved strategy research on operations management system for electric vehicles

Zhang¹,

Chen²,

Zhu³

et al. 2015

2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT)

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It is very important for Operations Management System (OMS) and big data analysis application to improve the data quality of Electric Vehicle (EV) charging service. This paper focuses on the charging transaction record data from the Beijing EV charging OMS, and analyzes error types and distributed locations of the abnormal data. Based on the mathematical logic among various kinds of operation data, the data selection rules and processing method are proposed, and the system improved scheme is given. Through the design and application of data selection and processing module, the abnormal data can be timely detected and corrected. It is also beneficial for the system operation and maintenance to improve the acquisition data quality. The comparative analysis results verify the feasibility and effectivity of the proposed scheme. This research is a necessary guarantee for the big data technology application. Keywords-electric vehicle; operations management system; big data; data quality; data selection and processingA. PURPOSES OF DATA SELECTION • •

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Resilient design of recharging station networks for electric transportation vehicles

Cited by 4 publications

References 17 publications

The Evolution of Computational Agency

The Evolution of Computational Agency

Multi-objective optimal path selection in electric vehicles

Data quality analysis and improved strategy research on operations management system for electric vehicles

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