Open AccessBook
Multiple Criteria Optimization: Theory, Computation, and Application
Reads0
Chats0
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.read more
Citations
More filters
Journal ArticleDOI
Solutions for the Portfolio Selection Problem with Interval and Fuzzy Coefficients
TL;DR: Investigating the properties of two efficiency conditions by means of preference cones and feasible region, it is discussed that the two kinds of solutions can be identified with the sets of combinations of lower or upper bounds of intervals.
Journal ArticleDOI
Some thoughts on research in multiple criteria decision making
TL;DR: Operations research is not only alive and well today, but it is hale and hardy, according to the doomsayers who feared the death of the field.
Journal ArticleDOI
An Interactive Method for 0-1 Multiobjective Problems Using Simulated Annealing and Tabu Search
M. João Alves,João Clímaco +1 more
TL;DR: This paper presents an interactive method for solving general 0-1 multiobjective linear programs using Simulated Annealing and Tabu Search and improves significantly the results of single objective problems and the quality of the potentially nondominated solutions generated for the multiobjectives problems.
Proceedings ArticleDOI
Reputation-Based Scheduling on Unreliable Distributed Infrastructures
TL;DR: This paper presents a model in which reliability is not a binary property but a statistical one based on a node’s prior performance and behavior, and uses this model to construct several reputation-based scheduling algorithms that employ estimated reliability ratings of worker nodes for efficient task allocation.
Journal ArticleDOI
A double-module immune algorithm for multi-objective optimization problems
TL;DR: A novel double-module immune algorithm named DMMO is presented, where two evolutionary modules are embedded to simultaneously improve the convergence speed and population diversity and performs better than the compared targets on most of test problems.