Inference-based optimization via simulation, which substitutes Gaussian process (GP) learning for the structural properties exploited in mathematical programming, is a powerful paradigm that has been shown to be remarkably effective problems of modest feasible-region size and decision-variable dimension. The limitation “modest” result computational overhead numerical challenges encountered comp...

this paper presents a new observer design methodology for a time varying actuator fault estimation. a new linear matrix inequality (lmi) design algorithm is developed to tackle the limitations (e.g. equality constraint and robustness problems) of the well known so called fast adaptive fault estimation observer (fafe). the fafe is capable of estimating a wide range of time-varying actuator fault...

always allocate man - machine is considered as one of the main concerns of operation planners. utilize an appropriate using of workforce in situations that the operation is dependent on the capabilities of the workforce, can lead to improve the systems efficiency and productivity indicators. since these issues, categories in np-hard problems, so optimization and solving the mathematical model o...

Artificial intelligence methods and search techniques such as simulated annealing and simultaneous perturbation can be used in simulation optimization of complex stochastic systems. Simultaneous perturbation has also been applied in the area of neural networks. Simulation optimization methods are often used when it is difficult to obtain the closed form of the objective function, but it is poss...

a strategy that integrates an organization's marketing goals into a cohesive whole, ideally drawn from market research, focuses on the ideal product mix in order to achieve maximum profit potential; the marketing strategy is set out as a marketing plan, a good idea, method, information, object, or service that is the end result of a process and serves as a need or want satisfier. it is usually ...

Optimization via simulation consists in applying iteratively two detached models until an optimality condition is reached: a simulation model for predicting the system performance and an optimization model for finding out the optimal solution. Mathematical programming representation has been recently used to describe the behavior of discrete event systems as well as their formal properties. Thi...

Many military planning problems are difficult to solve using pure mathematical programming techniques. One such problem is scheduling unmanned aerial vehicles (UAVs) in military operations subject to dynamic movement and control constraints. This problem is instead formulated as a dynamic programming problem whose approximate solution is obtained via the Assignment Scheduling Capability for UAV...