In this paper an approach in data envelopment analysis dealing with evaluation of non – zero slacks was propounded. This approach intends that weight of inefficient and weak efficient points that have been evaluated as zero weight, be considered as positive weight, and also in this approach, the pareto efficiency evaluates the picture of this point. In this approach positive weights in the i...

Rawls (1971, 1993) suggests that a primary goods index should be the basis for interpersonal comparisons in a theory of justice, but it is well known that in general this approach is not compatible with the Pareto principle. This is the indexing impasse. Sen (1985, 1991) argues that this is partly due to the fact that the approach does not take note of the citizen’s orderings of these bundles o...

Evolutionary algorithms EA have proved to be well suited for optimization prob lems with multiple objectives Due to their inherent parallelism they are able to capture a number of solutions concurrently in a single run In this report we propose a new evolutionary approach to multicriteria optimization the Strength Pareto Evolutionary Algorithm SPEA It combines various features of previous multi...

ABSTRACT This research mainly aims to provide a practical method (1) rank firm's customers, (2) determine the implied uncertainty for customers’ demand, and (3) allocate supply chain (SC) resources meeting customer demands in an equitable manner order them with appropriate/desired level of responsiveness. The suggested methodology utilizes analytic hierarchy process (AHP) Delphi assess customer...

Abstract– Uniformization is a powerful numerical technique for the analysis of continuous time Markov chains that possesses a probabilistic interpretation. In this paper, we propose the combination of the embedded Markov chain approach for queues with uniformization to analyze GI/M/s/c queues. The main steps of the procedure are: (1) To compute the mixed-Poisson probabilities associated to the ...

A multi-objective particle swarm optimization (MOPSO) approach is presented for generating Pareto-optimal solutions for reservoir operation problems. This method is developed by integrating Pareto dominance principles into particle swarm optimization (PSO) algorithm. In addition, a variable size external repository and an efficient elitist-mutation (EM) operator are introduced. The proposed EM-...

This paper discusses the implementation of local search in evolutionary multiobjective optimization (EMO) algorithms for the design of a simple but powerful memetic EMO algorithm. First we propose a basic framework of our memetic EMO algorithm, which is a hybrid algorithm of the NSGA-II and local search. In the generation update procedure of our memetic EMO algorithm, the next population is con...

Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) ( ) computational complexity (where is the number of objectives and is the population size); 2) nonelitism approach; and 3) the need for specifying a sharing parameter. In this paper, we suggest a nondominated sorting-based multiobjective EA (MOEA), called nondominate...