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A Linguistic Cloud-Based Consensus Framework with Three Behavior Classifications Under Trust-Interest Relations
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2023 | Group Decision and Negotiation | Citations: 0
Authors: Liu, Weiqiao; Zhu, Jianjun; Liu, Peide; Wang, Peng; Song, Wen

Abstract: In the consensus reaching process (CRP), experts are not infinitely compromising ... Expand

Abstract: In the consensus reaching process (CRP), experts are not infinitely compromising when adjusting opinions, and the “compromise limit” is used to reflect the limited compromise behaviors in the process of expert opinion adjustment. In complex and multiple social relations, experts tend to exhibit different behaviors according to their compromise limits. This paper aims to develop a novel CRP framework to promote a consensus that categorizes and manages experts based on their compromise limits. Firstly, the trust-interest network is defined to represent the multiple relations among experts, and the expert weights are calculated by considering the impact of interest manipulation on trust relations. Secondly, a novel cloud model-based minimum cost consensus model is established, which considers the mutual acceptance between the experts and the group, as well as the changes in the ranges of experts’ hesitation and the collective acceptance. Thirdly, three behavior classifications are defined based on individual compromise limits and group acceptance ranges: cooperative behavior, hesitating non-cooperative behavior, and strong non-cooperative behavior, and a CRP optimization model is constructed to manage the three behaviors. Finally, a numerical example is given to illustrate the validity and superiority of the proposed model. Collapse

Semantic filters: optimization theory

Topics: cloud computing social network decision making

Methods: optimization model cross sectional research group interview computational algorithm social network analysis

Theories: optimization theory
Will they take this offer? A machine learning price elasticity model for predicting upselling acceptance of premium airline seating
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2023 | Information & Management | Citations: 0
Authors: Thirumuruganathan, Saravanan; Emadi, Noora Al; Jung, Soon-gyo; Salminen, Joni; Robillos, Dianne Ramirez; Jansen, Bernard J.

Abstract: Employing customer information from one of the world’s largest airline companies ... Expand

Abstract: Employing customer information from one of the world’s largest airline companies, we develop a price elasticity model (PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer’s acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years mirroring actual data indicates that PREM implementation results in approximately 1.12 million (7.94%) fewer non-relevant customer email messages, a predicted increase of 72,200 (37.2%) offers accepted, and an estimated $72.2 million (37.2%) of increased revenue. Our results illustrate the potential of automated pricing information and targeting marketing messages for upselling acceptance. We also identified three customer segments: (1) Never Upgrades are those who never take the upgrade offer, (2) Upgrade Lovers are those who generally upgrade, and (3) Upgrade Lover Lookalikes have no historical record but fit the profile of those that tend to upgrade. We discuss the implications for airline companies and related travel and tourism industries. Collapse

Semantic filters: optimization theory

Topics: electronic mail marketing management price management customer satisfaction application programming interface

Methods: autoencoder data transformation machine learning experiment principal component analysis

Theories: optimization theory decision theory
Autonomous Energy Grids

2018 | HICSS | Citations: 0
Authors: Kroposki, Benjamin; Dall'Anese, Emiliano; Bernstein, Andrey; Zhang, Yingchen; Hodge, Bri-Mathias

Abstract: Current frameworks to monitor, control, and optimize large-scale energy systems ... Expand

Abstract: Current frameworks to monitor, control, and optimize large-scale energy systems are becoming increasingly inadequate because of significantly high penetration levels of variable generation and distributed energy resources being integrated into electric power systems; the deluge of data from pervasive metering of energy grids; and a variety of new market mechanisms, including multilevel ancillary services. This paper outlines the concept of autonomous energy grids (AEGs). These systems are supported by a scalable, reconfigurable, and self-organizing information and control infrastructure, are extremely secure and resilient (self-healing), and can self-optimize in real time to ensure economic and reliable performance while systematically integrating energy in all forms. AEGs rely on cellular building blocks that can self-optimize when isolated from a larger grid and participate in optimal operation when interconnected to a larger grid. This paper describes the key concepts and research necessary in the broad domains of optimization theory, control theory, big data analytics, and complex system theory and modeling to realize the AEG vision. Collapse

Semantic filters: optimization theory

Topics: big data

Methods: optimization model computational algorithm machine learning

Theories: general systems theory control theory optimization theory
Towards Distributed Cognitive Expert Systems
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2017 | DESRIST | Citations: 7
Authors: Tofangchi, Schahin; Hanelt, Andre; Kolbe, Lutz M.

Abstract: The process of Datafication gives rise to ubiquitousness of data. Data-driven ap ... Expand

Abstract: The process of Datafication gives rise to ubiquitousness of data. Data-driven approaches may create meaningful insights from the vast volumes of data available to businesses. However, coping with the great volume and variety of data requires improved data analysis methods. Many such methods are dependent on a user’s subjective domain knowledge. This dependency leads to a barrier for the use of sophisticated statistical methods, because a user would have to invest a significant amount of labor into the customization of such methods in order to incorporate domain knowledge into them. We argue that machines may efficiently support researchers and analysts even with non-quantitative data once they are equipped with the ability to develop their own subjective domain knowledge in a way that the amount of manual customization is reduced. Our contribution is a design theory – called the Division-of-Labor Framework – for generating and using Experts that can develop domain knowledge. Collapse

Semantic filters: optimization theory

Topics: artificial intelligence big data expert system analytical information system implicit knowledge

Methods: design theory optimization model computational algorithm information extraction design artifact

Theories: optimization theory
An efficient resource allocation for maximizing benefit of users and resource providers in ad hoc grid environment
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2012 | Information Systems Frontiers | Citations: 1
Authors: Li, Chunlin; Li, Layuan

Abstract: Ad hoc grids allow a group of individuals to accomplish a mission that involves ... Expand

Abstract: Ad hoc grids allow a group of individuals to accomplish a mission that involves computation and communication among the grid components, often without fixed structure. In an ad hoc grid, every node in the network can spontaneously arise as a resource consumer or a resource producer at any time when it needs a resource or it possesses an idle resource. At the same time, the node in ad hoc grid is often energy constrained. The paper proposes an efficient resource allocation scheme for grid computing marketplace where ad hoc grid users can buy usage of memory and CPU from grid resource providers. The ad hoc grid user agents purpose to obtain the optimized quality of service to accomplish their tasks on time with a given budget, and the goal of grid resource providers as profit-maximization. Combining perspectives of both ad hoc grid users and resource providers, the paper present ad hoc grid resource allocation algorithm to maximize the global utility of the ad hoc grid system which are beneficial for both grid users and grid resource providers. Simulations are conducted to compare the performance of the algorithms with related work. Collapse

Semantic filters: optimization theory

Topics: resource allocation grid computing mobile system price management

Methods: computational algorithm simulation literature study optimization model experiment

Theories: optimization theory
Collaboration among mobile agents for efficient energy allocation in mobile grid
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2012 | Information Systems Frontiers | Citations: 2
Authors: Li, Chunlin; Li, Layuan

Abstract: The use of mobile devices in grid environments may have two interaction aspects: ... Expand

Abstract: The use of mobile devices in grid environments may have two interaction aspects: devices are considered as users of grid resources or as grid resources providers. Due to the limitation constraints on energy and processing capacity of mobile devices, their integration into the Grid is difficult. In this paper, we investigate the cooperation among mobile devices to balance the energy consumption and computation workloads. Mobile devices can have different roles such as buyer devices and seller devices. In the mobile grid, the energies of mobile devices are uneven, energy-poor devices can exploit other devices with spare energy. Our model consists of two actors: A buyer device agent represents the benefits of mobile buyer device that intends to purchase energy from other devices. A seller device agent represents the profits of mobile seller device that is willing to sell spare energy to other devices. The objective of optimal energy allocation in mobile grid is to maximize the utility of the system without exceeding the energy capacity, expense budget and the deadline. A collaboration algorithm among mobile agents for efficient energy allocation is proposed. In the simulation, the performance evaluation of collaboration algorithm among mobile agents is conducted. Collapse

Semantic filters: optimization theory

Topics: mobile system willingness to pay resource allocation mobile business

Methods: computational algorithm optimization model simulation experiment literature study

Theories: optimization theory

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Found 6 articles

A Linguistic Cloud-Based Consensus Framework with Three Behavior Classifications Under Trust-Interest Relations

Will they take this offer? A machine learning price elasticity model for predicting upselling acceptance of premium airline seating

Autonomous Energy Grids

Towards Distributed Cognitive Expert Systems

An efficient resource allocation for maximizing benefit of users and resource providers in ad hoc grid environment

Collaboration among mobile agents for efficient energy allocation in mobile grid