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
Coupling input–output analysis with multiobjective linear programming models for the study of economy–energy–environment–social (E3S) trade-offs: a review
TL;DR: This paper is aimed at reviewing the different modelling approaches available in the scientific literature based on coupling IO analysis with multiobjective models, which can be particularly useful for policy makers to assess the trade-offs between the economy–energy–environment–social pillars of sustainable development, particularly relevant in the current sluggish economic context.
Journal ArticleDOI
Mesta: An internet-based decision-support application for participatory strategic-level natural resources planning
TL;DR: The main benefit of Mesta as a decision-support tool during the negotiation process of the group was that the participants were forced to merge their preferences with the realistic production possibilities of the planning regions.
Journal ArticleDOI
A metaheuristic algorithm to solve the selection of transportation channels in supply chain design
TL;DR: This paper addresses a supply chain design problem based on a two-echelon single-product system and developed a metaheuristic algorithm that combines principles of greedy functions, Scatter Search, Path Relinking and Mathematical Programming to solve the problem.
Journal ArticleDOI
Complete solutions of multiple objective transportation problems with possibilistic coefficients
TL;DR: The parametric analysis is used to decompose the parametric space of the equivalent problem and incorporate possibilistic data into the coefficients of objectice functions.
Journal ArticleDOI
Dependency solving
TL;DR: It is shown that the complexity of the underlying upgrade planning problem is NP-complete even for seemingly simple component models, and it is argued that the principal source of complexity lies in multiple available versions of components.