Productivity Optimization by Optimal Allocation of Human Resources with Application in Real Case at the Wagons Maintenance of Iron Ore Rail Transport

In the current industry the search for process optimization has been more and more constant, however many times this practice proves to be quite complex given the number of variables involved, an example of this is the case where from a heterogeneous group of workers want to define the best set of work pairs so that the collective productivity is as high as possible. In situations like this, the use of the metaheuristic genetic algorithm becomes quite attractive, since in the literature there are many examples of its use in the optimization of non-linear problems, with continuous and discrete characteristics of the control variables and with an exponential increase in the number possible solutions, in addition to the flexibility to incorporate the real problem constraints into the solution. In this context, this study codified a problem of real case for the definition of work teams in a mining wagon maintenance workshop. In the theoretical simulation stage, using historical team performance data, the genetic algorithm indicated a 22 percent better solution when compared to the random choice of work teams. Finally, the solution suggested by the genetic algorithm was implemented in the field, resulting in a performance increase of 7.9percent.