Testing a walkthrough methodology for theory-based design of walk-up-and-use interfaces
CHI '90 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A mathematical model of the finding of usability problems
INTERCHI '93 Proceedings of the INTERCHI '93 conference on Human factors in computing systems
The cognitive walkthrough method: a practitioner's guide
Usability inspection methods
Estimating the number of subjects needed for a thinking aloud test
International Journal of Human-Computer Studies
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
An exploratory cognitive DSS for strategic decision making
Decision Support Systems
IEEE Computer Graphics and Applications
Challenges in Visual Data Analysis
IV '06 Proceedings of the conference on Information Visualization
Guidelines for Eliciting Usability Functionalities
IEEE Transactions on Software Engineering
Usability evaluation considered harmful (some of the time)
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multiobjective Optimization: Interactive and Evolutionary Approaches
Multiobjective Optimization: Interactive and Evolutionary Approaches
Introduction to Multiobjective Optimization: Noninteractive Approaches
Multiobjective Optimization
Real-World Applications of Multiobjective Optimization
Multiobjective Optimization
IEEE Symposium on Visual Analytics Science and Technology 2007
VAST '07 Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology
Wastewater treatment: New insight provided by interactive multiobjective optimization
Decision Support Systems
| Hi-index | 12.05 |
Interactive multiobjective optimization (IMO) is a subfield of multiple criteria decision making. In multiobjective optimization, the optimization problem is formulated with a mathematical model containing several conflicting objectives and constraints depending on decision variables. By using IMO methods, a decision maker progressively provides preference information in order to find the most satisfactory compromise between the conflicting objectives. In this paper, we consider implementation challenges of IMO methods. In particular, we consider what kind of interaction techniques can support the decision making process and information exchange between IMO methods and the decision maker. The implementation of an IMO method called Pareto Navigator is used as an example to demonstrate concrete challenges of interaction design. This paper focuses on describing the incremental development of the user interface for Pareto Navigator including empirical validation by user testing evaluation.