Optimization of Vaccine Production in Workshop Based on Genetic Algorithm

Abstract

In the face of some infectious diseases raging all over the world, the efficiency of vaccine production directly affects the ability of medical staff to rescue patients and the timeliness of global epidemic prevention. Therefore, it is of great significance to establish an optimal planning model from the production source to minimize the time required for vaccine production without affecting the quality of vaccine production. This paper analyzes and discusses the problem of vaccine production from two aspects. Firstly, under ideal conditions, without considering the impact of vaccine production time, based on the average time of producing each box of vaccine at each station, a scheduling optimization model with multi-objective constraints is established, and then the flow shop scheduling model (FSP) of genetic algorithm is used to solve it. We estimate that the gene assignment adopts a classical roulette algorithm, the gene crossover part adopts the partial mapping crossover algorithm, and the gene mutation step uses the gene reverse order algorithm to simulate the operation of the stack. In practice, the time required to produce each vaccine at each station is random. Bring in the normal probability distribution function, improve the genetic algorithm and shorten the total time by 5%.