Multicriteria Optimization and Decision Making: Principles, Algorithms and Case Studies
By Michael T. M. Emmerich, André H. Deutz. 2024
648 citations recorded by the source corpus.
Real-world decision and optimization problems, often involve constraints and conflicting criteria. For example, choosing a travel method must balance speed, cost, environmental footprint, and convenience. Similarly, designing an industrial process must consider safety, environmental impact, and cost efficiency. Ideal solutions where all objectives are optimally met are rare; instead, we seek good compromises and aim to avoid lose-lose scenarios. Multicriteria optimization offers computational techniques to compute Pareto optimal solutions, aiding decision analysis and decision making. This reader offers an introduction to this topic and has been developed on the basis of the revised edition of the reader for the MSc computer science course"Multicriteria Optimization and Decision Analysis"at the Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands. This course was taught annually by the first author from 2007 to 2023 as a single semester course with lectures and practicals. Our aim was to make the material accessible to MSc students who do not study mathematics as their core discipline by introducing basic numerical analysis concepts when necessary and providing numerical examples for interesting cases. The introduction is organized in a unique didactic manner developed by the authors, starting from more simple concepts such as linear programming and single-point methods, and advancing from these to more difficult concepts such as optimality conditions for nonlinear optimization and set-oriented solution algorithms. Besides, we focus on the mathematical modeling and foundations rather than on specific algorithms, though not excluding the discussion of some representative examples of solution algorithms.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computational Theory and Mathematics
In Control and Systems Engineering
Citations in the corpus
- Multiobjective Optimization (2008, positive): “This latter step is the topic of multicriteria decision analysis (Figueira, Greco, and Ehrgott 2005).”
- Evolutionary Large-Scale Multi-Objective Optimization: A Survey (2021, methodology): “According to the Karush-Kuhn-Tucker condition [42], the Pareto optimal set of an m -objective optimization problem is an ( m − 1 ) -dimensional piecewise continuous manifold.”
- Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces (2019, neutral): “1) In most cases, (13) only contains one optimal solution and under this condition an optimization method only needs to focus on this optimal solution [47].”