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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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Book ChapterDOI
Chapter 5 – Multiobjective Optimization and Advanced Topics
TL;DR: An area of multiple-criteria decision-making, concerning mathematical optimization problems involving more than one objective functions to be optimized simultaneously, where optimal decisions need to be taken in the presence of trade-offs between two or more objectives that may be in conflict.
Journal ArticleDOI
A genetic algorithm for multiobjective dangerous goods route planning
TL;DR: A multiobjective genetic algorithm (MOGA) for the determination of optimal routes for DG transportation under conflicting objectives is developed and applied to the transportation of liquefied petroleum gas in the road network of Hong Kong.
Journal ArticleDOI
Decision-making model for supporting supply chain efficiency evaluation
TL;DR: This paper presents some approach for the formulation of the decision-making model in supporting the assessment of supply chain efficiency and proposed indicators for assessing the quality of functioning.
Journal ArticleDOI
A flexible programming approach based on intuitionistic fuzzy optimization and geometric programming for solving multi-objective nonlinear programming problems
TL;DR: A novel method is proposed to support the process of solving multi-objective nonlinear programming problems subject to strict or flexible constraints and it is concluded that the resulting solution vectors simultaneously satisfy both of the conditions of intuitionistic fuzzy efficiency and Pareto-optimality.
Journal ArticleDOI
Connectedness of Efficient Solutions in Multiple Objective Combinatorial Optimization
TL;DR: It is shown that many classical multiple objective combinatorial optimization problems do not possess the connectedness property in general, including, among others, knapsack problems (and even several special cases) and linear assignment problems.