Simulation-based meta-heuristic approach for booking limits problem at a hotel baby

In this work, a meta-heuristic approach integrated the genetic algorithm (GA) with ranking and selection (R&S) is proposed to solve for a good enough solution of the hard stochastic simulation optimization problem (SSOP) with huge solution space. First, a rough model is used as a fitness evaluation in the GA to select N roughly good solutions from entire solution space. Then, we proceed with the R&S policy to search for a good enough solution from the N roughly good solutions. Finally, the meta-heuristic approach is applied to a booking limits problem at a hotel baby, which is formulated as a hard SSOP that consists of a huge solution space comprised by the vector of booking limits. The vector of good enough booking limits obtained by the proposed approach is promising in the aspects of solution quality and computational efficiency.