Two techniques are combined during the design of an optimal controller: Linear Matrix Inequalities (LMIs) and Multi-objective Genetic Algorithms (MOGAs). In this paper the LMI optimization technique is used to obtain a single controller while MOGA is used to convert the controller design into a multi-objective optimization procedure. The combination of these techniques is proposed in this docum...

A new version of an interactive NIMBUS method for nondifferentiable multiobjective optimization is described. It is based on the reference point idea and the classification of the objective functions. The original problem is transformed into a single objective form according to the classification information. NIMBUS has been designed especially to be able to handle complicated real-life problem...

Control charts are the most important tools of statistical process control used to discriminate between assignable and common causes of variation and to improve the quality of a process. To design a control chart, three parameters including sample size, sampling interval, and control limits should be determined. The objectives are hourly expected cost, in-control average run length, power of th...

With the increasing rate of population in big cities around world, tendency to build new buildings suburb main or large apartments has been highlighted. In this regard, building residential complexes seen a dramatic increase these areas as it makes possible number units within reasonable space. Although have brought numerous benefits, they are some challenges regarding their construction proces...

This paper presents a strategy based on fuzzy logic and lexicographic multi-objective optimization for designing noise abatement procedures aimed at reducing the noise exposure of the population living around the airports. A non-linear multi-objective optimal control problem is formally written and identified. A lexicographic optimization technique is presented for establishing a hierarchical o...

In this paper, we develop a multi-objective model to optimally control the lead time of a multi-stage assembly system, using genetic algorithms. The multi-stage assembly system is modelled as an open queueing network. It is assumed that the product order arrives according to a Poisson process. In each service station, there is either one or infinite number of servers (machines) with exponential...