A Decision Making Framework for Sediment Assessment Developed for the Great Lakes

2002 ◽  
Vol 8 (7) ◽  
pp. 1641-1655 ◽  
Author(s):  
Lee Grapentine ◽  
Janette Anderson ◽  
Duncan Boyd ◽  
G. Allen Burton ◽  
Conrad DeBarros ◽  
...  
Keyword(s):  
Decision Making ◽  
Great Lakes ◽  
Sediment Assessment ◽  
Decision Making Framework

A BIM-based decision-making framework for optimal seismic retrofit of existing buildings

2021 ◽  
Vol 242 ◽  
pp. 112544
Author(s):  
Nicola Caterino ◽  
Iolanda Nuzzo ◽  
Antonio Ianniello ◽  
Giorgio Varchetta ◽  
Edoardo Cosenza
Keyword(s):  
Decision Making ◽  
Seismic Retrofit ◽  
Existing Buildings ◽  
Decision Making Framework

scholarly journals Online Learning Algorithms for the Real-Time Set-Point Tracking Problem

2021 ◽  
Vol 11 (14) ◽  
pp. 6620
Author(s):  
Arman Alahyari ◽  
David Pozo ◽  
Meisam Farrokhifar
Keyword(s):  
Decision Making ◽  
Power Systems ◽  
Real Time ◽  
Demand Response ◽  
Tracking Problem ◽  
Smart Meters ◽  
Set Point ◽  
Point Tracking ◽  
Power Set ◽  
Decision Making Framework

With the recent advent of technology within the smart grid, many conventional concepts of power systems have undergone drastic changes. Owing to technological developments, even small customers can monitor their energy consumption and schedule household applications with the utilization of smart meters and mobile devices. In this paper, we address the power set-point tracking problem for an aggregator that participates in a real-time ancillary program. Fast communication of data and control signal is possible, and the end-user side can exploit the provided signals through demand response programs benefiting both customers and the power grid. However, the existing optimization approaches rely on heavy computation and future parameter predictions, making them ineffective regarding real-time decision-making. As an alternative to the fixed control rules and offline optimization models, we propose the use of an online optimization decision-making framework for the power set-point tracking problem. For the introduced decision-making framework, two types of online algorithms are investigated with and without projections. The former is based on the standard online gradient descent (OGD) algorithm, while the latter is based on the Online Frank–Wolfe (OFW) algorithm. The results demonstrated that both algorithms could achieve sub-linear regret where the OGD approach reached approximately 2.4-times lower average losses. However, the OFW-based demand response algorithm performed up to twenty-nine percent faster when the number of loads increased for each round of optimization.

A Decision-Making Framework for Inventory Positioning in an Omnichannel Business Environment

2019 ◽  
Vol 12 (1) ◽  
pp. 81-94 ◽  
Author(s):  
Albert Wee Kwan Tan ◽  
David Gligor
Keyword(s):  
Decision Making ◽  
Business Model ◽  
Business Environment ◽  
Shopping Experience ◽  
Decision Making Framework ◽  
Customer Orders

Omnichannel is an evolving business model that has been gaining increased popularity among retailers. This business model allows firms to use a variety of channels to interact with their customers and fulfill their orders. Customers can order online and pick up later in the store, or they can choose to have the products delivered from a nearby store. Due to the complexity of fulfilling customer orders via omnichannel models, positioning inventory is a key challenge in supporting this type of business model. This article presents a framework for assisting companies in deciding under what condition to centralize or decentralize their inventory to fulfill customer orders without disrupting the shopping experience.

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