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Multiple Criteria Optimization: Theory, Computation, and Application

R. S. Laundy
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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.

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A review of robust optimal design and its application in dynamics

TL;DR: The robust design of a vibration absorber with mass and stiffness uncertainty in the main system is used to demonstrate the robust design approach in dynamics as discussed by the authors, and the results show a significant improvement in performance compared with the conventional solution.
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Multi‐objective combinatorial optimization problems: A survey

TL;DR: The present paper is intended to review the existing literature on multi-objective combinatorial optimization (MOCO) problems and examines various classical combinatorials problems in a multi-criteria framework.
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Supply Chain Risk, Simulation, and Vendor Selection

TL;DR: The results show that the proposed approach allows decision makers to perform trade-off analysis among expected costs, quality acceptance levels, and on-time delivery distributions and provides alternative tools to evaluate and improve supplier selection decisions in an uncertain supply chain environment.
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Applications of multiobjective optimization in Chemical Engineering

TL;DR: The general background of this area is presented, followed by a description of how the results can be described in terms of Pareto sets, and the several methods available for generating optimal solutions.
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A survey of recent developments in multiobjective optimization

TL;DR: Recent developments in Multiobjective Optimization are discussed, including optimality conditions, applications, global optimization techniques, the new concept of epsilon Pareto optimal solution, and heuristics.