Research on the calculation method of global optimal solution for multivariate functions based on genetic algorithm

In this paper, the time delay of warehouse entry, traveling distance, scheduled arrival time, and other factors are fully considered. From there, a genetic algorithm global optimal solution is conceived for the computation of the warehouse entry allocation problem. We propose a new constrained optimization algorithm, based on orthogonal experimental design, to address the global optimal solution of multivariate functions. We also verified the algorithm’s effectiveness and usability in a practical simulation study of the warehouse entry allocation problem. The optimal value of the genetic algorithm is 9.3487. In the parameter design, the actual total number of demands is 131. The S, T, and D of the calculation results of secondary simulation experiments have been reduced. Therefore, the global optimization of the genetic algorithm enhances the shelf stability and boosts the efficiency of the work in the store’s goods allocation.