2022
DOI: 10.1109/access.2022.3161465
|View full text |Cite
|
Sign up to set email alerts
|

Interactivized: Visual Interaction for Better Decisions With Interactive Multiobjective Optimization

Abstract: In today's data-driven world, decision makers are facing many conflicting objectives. Since there is usually no solution that optimizes all objectives simultaneously, the aim is to identify a solution with acceptable trade-offs. Interactive multiobjective optimization methods are iterative processes in which a human decision maker repeatedly provides one's preferences to request computing new solutions and compares them. With these methods, the decision maker can learn about the problem and its limitations. Ho… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
4
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
4
2
1

Relationship

0
7

Authors

Journals

citations
Cited by 7 publications
(4 citation statements)
references
References 56 publications
(59 reference statements)
0
4
0
Order By: Relevance
“…Coaches who cannot call on experts could be supported by feedback through a coach dashboard to gain insights on training load and subjective measures in a simple, feasible, and informative way [ 16 , 17 ]. Data science and business intelligence facilitate the development of interactive dashboards that help users to analyse and interpret large quantities of data [ 18 ]. Equipping the coach with a coach dashboard would enable informed, science-based, and personalised decision making for training optimisation.…”
Section: Introductionmentioning
confidence: 99%
“…Coaches who cannot call on experts could be supported by feedback through a coach dashboard to gain insights on training load and subjective measures in a simple, feasible, and informative way [ 16 , 17 ]. Data science and business intelligence facilitate the development of interactive dashboards that help users to analyse and interpret large quantities of data [ 18 ]. Equipping the coach with a coach dashboard would enable informed, science-based, and personalised decision making for training optimisation.…”
Section: Introductionmentioning
confidence: 99%
“…In this architecture, a customized IDSS combining elements of MCDSS and IDSS is considered where the IDSS provides methods such as clustering, partitioning, decision trees, and Flexible Pattern Mining (FPM) to use for knowledge extraction [28]. Several examples of an IDSS can be found [29,15,8]. To simplify modifications, an open-source IDSS as a base is preferred.…”
Section: Intelligent Decision-support Subsystemmentioning
confidence: 99%
“…To orient decision makers among the multiple alternatives and criteria, visualizations are commonly used as a part of the presentation component in MCDA-based decision methods. 29,30 MCDA models have also been integrated with geographical information systems (GIS) to support spatial decision-making through encoding the metrics and results of MCDA models in geographic visualizations. 31,32 Particularly, Jankowski discussed the architecture and implementation of GIS and MCDA through either loosely coupling them with file exchange module or tightly integrating the two with a shared user interface and database.…”
Section: Guidance Through Decision Supportmentioning
confidence: 99%