Upper and lower boundary convergence search simulation optimization calculation method with a screening mechanism

Abstract

The invention relates to an upper and lower boundary convergence search simulation optimization calculation method with a screening mechanism. The overall service satisfaction degree of the service system is maximized to serve as an optimization target.A mixed integer type optimization model of the two heterogeneous service type service systems with the screening mechanism is created. A system simulation value is searched by utilizing continuous convergence of an upper bound and a lower bound in a systematized mode. The searched upper bound or the searched lower bound in each iteration calculation is changed. A new threshold value is found through a dichotomy until the new threshold value is converged to a waiting time value conforming to a trust interval. According to the method, the approximate optimal solution of the optimization model can be found out under the limitation of very large solution space, time and sampling cost.

Description

technical field [0001] The invention relates to an upper and lower limit convergence search simulation optimization calculation method with a screening mechanism. Background technique [0002] The simulation optimization methods in the prior art focus on the development of related theories on how to converge to the optimal solution when the number of samples approaches infinity. For the simulation optimization model, in the limited number of samples, it is impossible to guarantee that all satisfying Feasible solutions of stochastic constraints. [0003] However, in practice, the size of the sample usually depends on the subjective decision of the decision maker, and only a limited number of samples can be used. At the same time, when the number of samples required for system simulation is larger, it means that the sampling cost is higher; and in the case of insufficient number of samples, simulation optimization cannot guarantee that a feasible solution can be found. In ad...

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Problems solved by technology

[0002] The simulation optimization methods in the prior art focus on the development of related theories on how to converge to the optimal solution when the number of samples approaches infinity. For the simulation optimization model, in the limited number of samples, it is impossible to guarantee that all satisfying Feasible solution of stochastic constraints

[0003] However, in practice, the size of the sample usually depends on the subjective decision of the decision maker, and only a limited number of samples can be used

At the same time, when the number of samples required for system simulation is larger, it means that the sampling cost is higher; and in the case of insufficient number of samples, simulation optimization cannot guarantee that a feasible solution can be found.

In addition, in dealing with optimization problems with discrete variables, it is necessary to estimate the subgradient function (Subgradient). The research work in the past literature often uses the finite difference method (FiniteDifferences), which often requires a huge calculation and simulation time cost.

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