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Multiple Criteria Optimization: Theory, Computation, and Application
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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
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Hybrid approach for solving multiple-objective linear programs in outcome space
TL;DR: This article presents and validate a new hybrid vector maximization approach for solving problem (MOLP) in outcome space that systematically integrates a simplicial partitioning technique into an outer approximation procedure to yield an algorithm that generates the set of all efficient extreme points in the outcome set of problem in a finite number of iterations.
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Fuzzy Goal Programming Procedure to Bilevel Multiobjective Linear Fractional Programming Problems
TL;DR: A fuzzy goal programming model to minimize the group regret of degree of satisfactions of both the decision makers is developed to achieve the highest degree of each of the defined membership function goals to the extent possible by minimizing their deviational variables and thereby obtaining the most satisfactory solution for both decision makers.
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Portfolio selection with skewness: A comparison of methods and a generalized one fund result
TL;DR: This contribution compares existing and newly developed techniques for geometrically representing mean-variance-skewness portfolio frontiers based on the rather widely adapted methodology of polynomial goal programming and a generalization of the well-known one fund separation theorem from traditional mean- variance portfolio theory.
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Performance of genetic programming to extract the trend in noisy data series
TL;DR: An approach based on genetic programming for forecasting stochastic time series using the MIB30 Index series and a multiobjective scheme relying on statistical properties of the faced series.