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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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Journal ArticleDOI

Equity portfolio management within the MCDM frame: a literature review

TL;DR: In this article, the authors provide a categorised bibliography on the application of the techniques of multiple criteria decision making (MCDM) to the problems and issues of portfolio management, and stress the inarguable multiple criterion nature of the majority of the problems that modern financial management faces.
Journal ArticleDOI

A bi-objective modeling approach applied to an urban semi-desirable facility location problem

TL;DR: A mixed-integer, bi-objective programming approach to identify the locations and capacities of semi-desirable (or semi-obnoxious) facilities and shows that this approach can be applied to a real-world planning scenario.
Proceedings Article

Developments in Multi-Attribute Portfolio Selection

TL;DR: In this paper, the authors reconcile why it is possible that people in finance view conventional portfolio selection as a single criterion problem and people in multiple criteria optimization view it as a bi-criterion problem, and show how, for more complex investors, the theory of mean-variance portfolio selection can be extended to include additional objectives such as dividends, liquidity, turnover, number of securities in a portfolio, and so forth.
Book ChapterDOI

Chapter 8 Mathematical programming models and methods for production planning and scheduling

TL;DR: This chapter presents stochastic programming with recourse model that explicitly treats uncertainties regarding demand, fuel costs, and environmental restrictions.

Location of semi-obnoxious facilities

TL;DR: A critical overview of the mathematical methods commonly used in facility location models, which shows which are more realistic, but are usually also much less tractable from a computational viewpoint.