Open AccessBook
Multiple Criteria Optimization: Theory, Computation, and Application
Reads0
Chats0
TLDR
Mathematical Background Topics from Linear Algebra Single Objective Linear Programming Determining all Alternative Optima Comments about Objective Row Parametric Programming Utility Functions, Nondominated Criterion Vectors and Efficient Points Point Estimate Weighted-sums Approach.Abstract:
Mathematical Background Topics from Linear Algebra Single Objective Linear Programming Determining all Alternative Optima Comments about Objective Row Parametric Programming Utility Functions, Nondominated Criterion Vectors and Efficient Points Point Estimate Weighted-sums Approach Optimal Weighting Vectors, Scaling and Reduced Feasible Region Methods Vector-Maximum Algorithms Goal Programming Filtering and Set Discretization Multiple Objective Linear Fractional Programming Interactive Procedures Interactive Weighted Tchebycheff Procedure Tchebycheff/Weighted-Sums Implementation Applications Future Directions Index.read more
Citations
More filters
Journal ArticleDOI
A pareto race
Pekka Korhonen,Jyrki Wallenius +1 more
TL;DR: A dynamic and visual “free‐search” type of interactive procedure for multiple‐objective linear programming that enables a decision maker to freely search any part of the efficient frontier by controlling the speed and direction of motion.
Journal ArticleDOI
On scalarizing functions in multiobjective optimization
Kaisa Miettinen,Marko M. Mäkelä +1 more
TL;DR: A collection of scalarizing functions that have been used in interactive methods as well as some modifications are presented and their theoretical properties and numerical behaviour are compared.
Proceedings ArticleDOI
Summarizing sporting events using twitter
TL;DR: An algorithm that generates a journalistic summary of an event using only status updates from Twitter as a source is described, and the results are superior to the previous algorithm and approach the readability and grammaticality of the human-generated summaries.
BookDOI
Model-Based Decision Support Methodology with Environmental Applications
J Jaap Wessels,Marek Makowski +1 more
TL;DR: In this article, the authors present a decision support methodology for strategic environmental decision problems, and provide several generic as well as specific tools to support the analysis of compromise solutions that correspond best to decision maker preferences, allowing the use of other modeling concepts like soft constraints, soft simulation, or inverse simulation.
Journal ArticleDOI
Preference programming for robust portfolio modeling and project selection
TL;DR: The Robust Portfolio Modeling methodology is developed, which extends Preference Programming methods into portfolio problems where a subset of project proposals are funded in view of multiple evaluation criteria, and a project-level index is proposed to convey which projects are robust choices and how continued activities in preference elicitation should be focused.