Improved Beluga Whale Optimization for Solving the Simulation Optimization Problems with Stochastic Constraints

Simulation optimization problems with stochastic constraints are deterministic cost functions subject to constraints. Solving the considered problem by traditional approaches is time-consuming if search space large. In this work, an approach integration of beluga whale and ordinal presented resolve in a relatively short time frame. The proposed composed three levels: emulator, diversification, intensification. Firstly, polynomial chaos expansion treated as emulator evaluate design. Secondly, improved seek N candidates from whole space. Eventually, advanced optimal computational effort allocation adopted determine superior design candidates. utilized number service providers for minimizing staffing costs while delivering specific level care emergency department healthcare. A practical example six cases used verify approach. CPU consumes less than one minute cases, which demonstrates that can meet requirement real-time application. addition, compared five heuristic methods. Empirical tests indicate efficiency robustness