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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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Journal ArticleDOI
Dynamic multi-attribute decision making model with grey number evaluations
TL;DR: This study proposes a dynamic decision making model which takes the TOPSIS technique as main structure, integrating the concepts of grey number and Minkowski distance function into it to deal with the uncertain information and aggregate the multi-period evaluations.
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Indicators of ill-conditioned data sets and model misspecification in data envelopment analysis: an extended facet approach
TL;DR: In this paper, the authors propose a set of indicators to determine whether or not the specification of the input and output space is supported by data in the sense that the variation in data is sufficient for estimation of a frontier of the same dimension as the input output space.
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A multiobjective genetic algorithm for radio network optimization
TL;DR: A genetic algorithm (GA) that aims to approximate the Pareto frontier of the problem, and has been implemented in parallel on a network of workstations to speed up the search.
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Use of vehicle signature analysis and lexicographic optimization for vehicle reidentification on freeways
TL;DR: This paper formulates and solves the vehicle reidentification problem as a lexicographic optimization problem with the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/destination demands.
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Incremental analysis for MCDM with an application to group TOPSIS
TL;DR: Two representations of cost, i.e., a cost index and utility index, are proposed in the model to better-fit real-world situations to understand the essentials of incremental analysis.