Otimização por algoritmo genético da produtividade em uma oficina de manutenção de vagões aplicada em caso real

Abstract

In today's industry, the search for process optimization has become increasingly common, but often this practice is quite complex given the number of variables involved, as in the case of defining the most productive work teams from a heterogeneous group of workers. In situations like this, the use of the metaheuristic genetic algorithm becomes attractive, since in the literature it presents many successful experiences with nonlinear problem optimization, with continuous and discrete control variables and with an exponential increase in the possible number of solutions, besides the flexibility to incorporate constraints of the problem into the solution. In this context, this work modeled a problem of work teams definition in a cargo wagon maintenance workshop of a mining company. In the simulation phase, using historical performance data of the teams, the genetic algorithm indicated an optimized solution 22% better than the random work team selection. Finally, the solution indicated by the genetic algorithm optimization was implemented in practice and comparing the results from the trimesters before and after the field tests, the optimization done improved in 7,9% the team performance.