Research On Stochastic Simulation Optimization Problems Based On Optimal Computing Budget Allocation

Stochastic simulation optimization (often shortened as simulation optimization) studies the optimization problem of simulation-based objectives, which using the result of the simulation model to estimate the actual performance of the system, and then an optimal or satisfactory solution is found by using the optimization algorithm. With the complexity of the problem at a deeper level, the amount of the computing in simulation is amazing. How to significantly reduce the amount of computing in simulation is the key to such problems, therefore, explore the problem of stochastic simulation optimization has important theoretical significance and application value. This paper introduced an optimal allocation algorithm based on optimal computing budget allocation and conducted a series of studies on the problem of selecting the best design from a discrete numbers of alternatives and the problem of selecting an optimal subset in the presence of stochastic constraints.On the basis of reading a large number of literatures, this paper proposes an idea based on computing budget allocation to solve the stochastic simulation optimization problem. A common approximation procedure used in simulation and statistics literature is adopted to estimates the probability of correct selection, which is based on the Bonferroni inequality. An asymptotic allocation rule is given to maximize the probability of correct selection through rational allocation of simulation resource, and thus greatly improving the efficiency of allocating simulation replications among competing designs. Finally, the simulation result shows the efficiency of the algorithm. Further, this paper provides an alternative approach based on the Optimal Computing Budget Allocation to tackle the problem of selecting an optimal subset in the presence of stochastic constraints. In the limited amount of computation, the top-m out of k designs based on simulated output can be identified and the probability of correct selection can be maximized by intelligent control simulation times of each design. Practice has proved that the algorithm is universal; it can be combined with simulation-based global optimization algorithm to further improve the search efficiency. Finally, the applications of stochastic simulation optimization in supply chain and logistics are discussed, a framework integrated with heuristic algorithm is given, and two examples of stochastic simulation optimization are provided.