## 4 Results  
This section summarizes the results obtained in the tests carried out to prove the performance of the algorithm. Figures 7-18 show the three best solutions found per launch of 100 generations, giving the fitness function value of each one of the solutions (coinciding with MHC) as well as the fulfillment of DMs' subjective preferences. Solutions with the minimum value of the fitness function and fulfilling most of the DM preferences are shown in bold. The best solutions shown in Figures 7-18 are summarized in Table 8. In all the cases tested, good designs were found considering the material handling cost and introducing the subjective preferences of all three DMs. It is worth mentioning the fact that new constraints have been introduced without needing to formulate them in a formal way and that the designs found do not only satisfy these quantitative criteria but obtain  
consider in a simple fitness function. Nonetheless, in the three cases tested, the loss of quantitative optimization seems not to be too significant, and in one of them, improvements have been found with respect to the best results previously known. Concretely, the best solution found in [10] for the Slaughterhouse problem had an MHC of 4136.46, while, according to Figures 7-18, considering the preferences of the three designers, a solution with 4245.51 was found, fulfilling 7 of the 9 requirements, and other one with 4574.24, fulfilling all nine requirements. In the same way, the best solution found in [10] for the CartonPacks problem had an MHC of 59.52, while here, a solution fulfilling 7 of the 9 requirements with a MHC of 57.29 has been found, and other one fulfilling all nine requirements and with an MHC of 68.19. Finally, for the ChoppedPlastic problem, the best solution found in [10] had an MHC of 257.94, while here, a solution fulfilling 7 of the 9 requirements has been found with an MHC of 269, and other one fulfilling all nine requirements with an MHC of 332.3. Furthermore, the most interesting issue of this paper is the collaborative intervention of more than one human designer to obtain a more complex and complete design. This possibility represents a substantial advance in this kind of problem since it has not been achieved before. The test strategy followed attempted to check whether the order in which the intervention of each one of the human designers is required affects the results or not. Therefore, a double method of intervention was used. Firstly, a more simple strategy was implemented, requiring the alternative intervention of the three human designers. This strategy requires the simultaneous presence of the three (\(m\), in a general way) human designers, in a way that could be fatiguing or, at least, annoying, since all the human designers have to be present in the same space simultaneously, while only one of them is actually working and the others are waiting. The second strategy does not require the simultaneous presence of the three designers, since each one of them guides the third part of the generations of the algorithm in a row, but this strategy could lead to the problem that the algorithm might _forget_ the preferences of the previous designer while following the current designer's preferences. Therefore, a set of tests has been performed to assess this, and the results are promising since there is no evidence that the order in which the designers perform their evaluations affects the results in any way. As shown in Table 8, in all cases, good solutions fulfilling the DMs' requirements have been found, sacrificing only a minor part of the material handling costs.