Genetic algorithm solving the scheduling problem of large-scale power grid material quality inspection bases under multiple constraints

Abstract

A mathematical programming model is constructed for the scheduling problem of large-scale power grid material quality inspection bases under complex and multi constraint conditions, with the completion time of material inspection, equipment utilization rate of the base, and total equipment load as multi-objective performance indicators. Based on the characteristics of the problem, a genetic algorithm is proposed as a tool for solving, and the encoding and decoding in the algorithm are specially designed for the detection base to ensure the generation of feasible solutions; Introducing roulette wheel to select populations, combining the characteristics of the problem to randomly mutate chromosomes, and using POX for chromosome crossover, in order to obtain the optimal solution to the problem. The proposed method effectively solves the scheduling problem of the testing base and greatly improves its operational efficiency by using the current quality testing example of dry-type transformers in the base for verification.