Many dynamic systems are based on a multiple number of parameters and relations dictating the outcome of the system. When the parameters are fully known, a closed solution can be found using regular optimization techniques, but when the information is partial or the parameters are random variables the estimation task is more complex. A well known approach for this problem is the Kalman filter widely used in the theory of guidance, navigation and control. Kalman filter is a recursive method for estimating multivariate systems and in the linear case the results are optimal. When the filter is applied to non-linear or static systems, one can still use the procedure with specific based variations. My research includes application of the Kalman filter and other optimization methods in the field of Artillery Fire Transfer, Stochastic Inventory models and health care models.
Bendersky, M., & David, I.(2016). The Full-Information best-choice problem with uniform or gamma horizons. Optimization, 65(4), (pp. 765-778).
Bendersky, M., & David, I. (2016). Deciding kidney-offer admissibility dependent on patients’ lifetime failure rate. European Journal of Operational Research, 251(2), (pp. 686-693).
Kriheli, B., Levner, E., Bendersky, M., Yakubov, E. (2015). A Fast Algorithm for Scheduling Detection-and-Rescue Operations Based on Data from Wireless Sensor Networks, Research in Computing Science, 104,(pp 9-21).