چكيده به لاتين
Based on the fact water management subject matter and considering cloud seeding as an emerged water achieving promising technique, this thesis provides an effective and innovative optimization approach for cloud seeding. In the first step, mathematical models include providing models for optimization in strategic decisions related to the location of cloud seeding facilities as well as operational planning in order to optimize operational decisions. In the next step, an integrated strategic-operational mathematical model is presented to improve the results of previous models. The integrated model presented with stochastic, fuzzy and Markovian techniques is presented as a non-deterministic stochastic-fuzzy-Markovian two-stage model, which will be solved using the Lagrangian relaxation technique. To be comprehensive, all these models include environmental and atmospheric conditions, including temperature, wind components and atmospheric stability, operational features of facilities, including seeding capacity and capability, types of seeding techniques, including ground-based and aerial seeding, and also the weakness and vulnerability of cloud seeding. Moreover, in this thesis, to provide an efficient mathematical modeling of the cloud seeding problem, novel point-to-polygon coverage algorithms are used to model ground-based and also line-to-polygon coverage algorithm to model aerial seeding, which fall under the umbrella of continuous coverage problems. Despite the mentioned models, they will be one of the innovative aspects of this thesis. The resulting models are developed as bi-objective mixed-integer linear programming, where the first objective function is to maximize the cloud seeding efficiency in covering continuous demand areas along with increasing the probability of precipitation, and the second objective function is to minimize the system-wide costs. Finally, the presented models are studied on Iran case study and the results are presented along with various analyses.
