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The purpose of this paper is to pursue two following main goals: first, theorizing a new concept named as equivalent bus load in order to make a promising simplification over power system analysis. Second, proposing an outstanding fast and simple approach based on introduced concept for voltage estimation after multiple component outages while satisfying required accuracy.

Equivalent load bus theory introduces three transfer matrices that describe power system topology. Mentioned matrices could be calculated simply after system reconfiguration without matrix inversion. Using transfer matrices a large-scale power system can be modeled by a simple two-bus power system from the viewpoint of any desired bus so that load flow calculation leads to same value. The analysis of simplified power system yields to extract a new incremental model based on equivalent bus load theory that will be distinguished as an outstanding fast method for voltage estimation aim.

A deep study for fast voltage estimation aim is dedicated to evaluate proposed method from the accuracy and quickness point of view and the outcomes are compared to a well-known method as Distribution Factors (DF). Results and computational times unveil that presented approach is more accurate and much faster.

A novel and new fast voltage estimation method for assessment of power system component outages is introduced.

This paper aims to consider a multi-site production planning problem with failure in rework and breakdown subject to demand uncertainty.

In this new mathematical model, at first, a feasible range for production time is found, and then the model is rewritten considering the demand uncertainty and robust optimization techniques. Here, three evolutionary methods are presented: robust particle swarm optimization, robust genetic algorithm (RGA) and robust simulated annealing with the ability of handling uncertainties. Firstly, the proposed mathematical model is validated by solving a problem in the LINGO environment. Afterwards, to compare and find the efficiency of the proposed evolutionary methods, some large-size test problems are solved.

The results show that the proposed models can prepare a promising approach to fulfill an efficient production planning in multi-site production planning. Results obtained by comparing the three proposed algorithms demonstrate that the presented RGA has better and more efficient solutions.

Considering the robust optimization approach to production system with failure in rework and breakdown under uncertainty.