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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

A bi-objective reverse logistics network analysis for post-sale service

TL;DR: This paper addresses the analysis of reverse logistic networks that deal with the returns requiring repair service, involving a manufacturer outsourcing to a third-party logistics (3PLs) provider for its post-sale services.
Journal ArticleDOI

A concept of the optimal solution of the transportation problem with fuzzy cost coefficients

TL;DR: A definition of the optimal solution of the transportation problem with fuzzy cost coefficients as well as an algorithm determining this solution are proposed.
Book ChapterDOI

Introduction to Multiobjective Optimization: Noninteractive Approaches

TL;DR: This chapter gives an introduction to nonlinear multiobjective optimization by covering some basic concepts as well as outlines of some methods where the decision maker either is not involved or specifies preference information before or after the actual solution process.
Journal ArticleDOI

Affordances as constraints on the control of stance

TL;DR: It is argued that different ways of controlling stance can have differing utility (affordances) for perception and action, and that the relationship between affordances and constraints on control actions should be investigated using geometrical methods.
Journal ArticleDOI

Normal Constraint Method with Guarantee of Even Representation of Complete Pareto Frontier

TL;DR: The normal constraint method is offered, which is a simple approach for generating Pareto solutions that are evenly distributed in the design space of an arbitrary number of objectives, and its critical distinction is defined, namely, the ability to generate a set of evenly distributed PareTo solutions over the complete Pare to frontier.