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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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A framework to minimise total energy consumption and total tardiness on a single machine
TL;DR: In this article, a greedy randomised multiobjective adaptive search metaheuristic is used to obtain an approximate pareto front (i.e. an approximate set of nondominated solutions).
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Selection of resilient supply portfolio under disruption risks
TL;DR: In this paper, a mixed integer programming approach is proposed to determine risk-neutral, risk-averse or mean-risk supply portfolios, with conditional value-at-risk applied to control the risk of worst-case cost.
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Multiobjective Optimization in Bioinformatics and Computational Biology
TL;DR: This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology and identifies five distinct "contexts," giving rise to multiple objectives.
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Response surface approximation of Pareto optimal front in multi-objective optimization
TL;DR: A systematic approach to approximate the Pareto optimal front (POF) by a response surface approximation is presented, and the approximated POF can help visualize and quantify trade-offs among objectives to select compromise designs.
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Spatial attributes and reserve design models: A review
TL;DR: An argument is made for the development of models that capture the dynamic interdependencies among sites and species populations and thus incorporate the reasons why spatial attributes are important.