scholarly journals Exploiting Relevance for Online Decision-Making in High-Dimensions

2021 ◽  
pp. 1-1
Author(s):  
Eralp Turgay ◽  
Cem Bulucu ◽  
Cem Tekin
Keyword(s):  
Decision Making ◽  
High Dimensions ◽  
Online Decision Making

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

scholarly journals The Role of Consumer Autonomy in Developing Sustainable AI: A Conceptual Framework

2021 ◽  
Vol 13 (4) ◽  
pp. 2332
Author(s):  
Lena Bjørlo ◽  
Øystein Moen ◽  
Mark Pasquine
Keyword(s):  
Decision Making ◽  
Decision Aids ◽  
Privacy Regulation ◽  
Advantages And Disadvantages ◽  
Fundamental Significance ◽  
Research Gap ◽  
Online Decision Making ◽  
Definition Of ◽  
Consumer Autonomy

Artificial intelligence (AI)-based decision aids are increasingly employed by businesses to assist consumers’ decision-making. Personalized content based on consumers’ data brings benefits for both consumers and businesses, i.e., with regards to more relevant content. However, this practice simultaneously enables increased possibilities for exerting hidden interference and manipulation on consumers, reducing consumer autonomy. We argue that due to this, consumer autonomy represents a resource at the risk of depletion and requiring protection, due to its fundamental significance for a democratic society. By balancing advantages and disadvantages of increased influence by AI, this paper addresses an important research gap and explores the essential challenges related to the use of AI for consumers’ decision-making and autonomy, grounded in extant literature. We offer a constructive, rather than optimistic or pessimistic, outlook on AI. Hereunder, we present propositions suggesting how these problems may be alleviated, and how consumer autonomy may be protected. These propositions constitute the fundament for a framework regarding the development of sustainable AI, in the context of online decision-making. We argue that notions of transparency, complementarity, and privacy regulation are vital for increasing consumer autonomy and promoting sustainable AI. Lastly, the paper offers a definition of sustainable AI within the contextual boundaries of online decision-making. Altogether, we position this paper as a contribution to the discussion of development towards a more socially sustainable and ethical use of AI.

Online Decision Making

2018 ◽  
pp. 1-25
Author(s):  
Helen Joanne Wall ◽  
Linda K. Kaye
Keyword(s):  
Decision Making ◽  
Computer Mediated Communication ◽  
Social Engineering ◽  
The Internet ◽  
Online Research ◽  
Mediated Communication ◽  
Computer Mediated ◽  
Online Behaviour ◽  
Online Decision Making ◽  
Buying Behaviour

The growth in computer-mediated communication has created real challenges for society; in particular, the internet has become an important resource for “convincing” or persuading a person to make a decision. From a cybersecurity perspective, online attempts to persuade someone to make a decision has implications for the radicalisation of individuals. This chapter reviews multiple definitions and theories relating to decision making to consider the applicability of these to online decision making in areas such as buying behaviour, social engineering, and radicalisation. Research investigating online decision making is outlined and the point is made that research examining online research has a different focus than research exploring online decision making. The chapter concludes with some key questions for scholars and practitioners. In particular, it is noted that online decision making cannot be explained by one single model, as none is sufficient in its own capacity to underpin all forms of online behaviour.

Online Decision-Making

2013 ◽  
pp. 99-119 ◽  
Author(s):  
Kevin Askew ◽  
Michael D. Coovert
Keyword(s):  
Decision Making ◽  
Online Decision Making

scholarly journals The Bayesian Prophet: A Low-Regret Framework for Online Decision Making

2020 ◽  
Author(s):  
Alberto Vera ◽  
Siddhartha Banerjee
Keyword(s):  
Decision Making ◽  
Big Data Analytics ◽  
General Technique ◽  
Matching Problems ◽  
Online Matching ◽  
Additive Loss ◽  
Online Decision Making ◽  
Resource Levels ◽  
Greedy Policy ◽  
New Framework

We develop a new framework for designing online policies given access to an oracle providing statistical information about an off-line benchmark. Having access to such prediction oracles enables simple and natural Bayesian selection policies and raises the question as to how these policies perform in different settings. Our work makes two important contributions toward this question: First, we develop a general technique we call compensated coupling, which can be used to derive bounds on the expected regret (i.e., additive loss with respect to a benchmark) for any online policy and off-line benchmark. Second, using this technique, we show that a natural greedy policy, which we call the Bayes selector, has constant expected regret (i.e., independent of the number of arrivals and resource levels) for a large class of problems we refer to as “online allocation with finite types,” which includes widely studied online packing and online matching problems. Our results generalize and simplify several existing results for online packing and online matching and suggest a promising pathway for obtaining oracle-driven policies for other online decision-making settings. This paper was accepted by George Shanthikumar, big data analytics.

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