Network design optimization from an availability perspective

Cankaya, Hakki C.; Lardies, A.; Ester, G.W.

doi:10.1109/netwks.2004.241088

Cited by 7 publications

(1 citation statement)

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“…Network design is an NP-hard problem [75,76] that has been studied in past decades by many network researchers [77][78][79][80][81][82][83][84][85][86]. The design process consists of growing the network from scratch including placement of nodes [80,81] and providing connectivity among nodes to enable services.…”

Section: F Design Of Complex Networked System For Optimal Performancementioning

confidence: 99%

A Data-Driven Decision Making Framework for Value-Based Engineering Design of Complex Network Systems

Shihab

Wei

Bloebaum

2019

AIAA Aviation 2019 Forum

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Many applications related to fields such as transportation, energy, communication, biology, and defense, require making several strategic and tactical decisions, which includes designing a real-world complex network system and setting its operational parameters. These systems typically have high uncertainties, a lack of a master design plan, and vulnerability to system failures that could potentially have catastrophic consequences. Therefore, we need rigorous theoretical foundations for designing complex networked systems to mitigate these risks and enhance the system performance. To our knowledge, based on literature survey, there has been little to no exploration of Big Data methods and tools in value-based systems engineering in how to better formulate the value function. Moreover, to our knowledge, there has been little to no work done in the network science community to develop a value function that would enable consistent assessment of robustness and resilience. At present, metrics have been developed to guide decisions in network design, but no overarching function, such as a value function, exists to enable understanding the true benefits of network robustness and resilience for system designer and stakeholders. This research paper aims to understand the factors and attributes contributing to the value of network performance with regards to robustness and resilience; explore the strategies to design complex networked systems with Big Data analytics; bridge the gap between the network science community and systems engineering community in the understanding of system robustness and resilience; and ultimately develop a mathematically rigorous design framework for complex networked systems, such as transportation networks, for generating designs that perform optimally in the presence of uncertainties based on the preference and risk attitude of the stakeholders. Network based companies, such as air carriers, can utilize this framework in their route planning, schedule planning and fleet planning, to design new route networks or reconfigure existing ones, for maximization of profit or other preferred objectives.

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