## 3 Experimentation  
The performance of the suggested multi-user interactive CRO is evaluated in this section. Although it is difficult to compare the results with other approaches since this is the first time that a multi-user strategy has been implemented in UA-FLP, the results obtained have been compared with those previously obtained in a single-user strategy [10]. The three problem instances chosen for evaluating the algorithm's performance are the following ones: _Slaughterhouse_, proposed in [PERSON] et al. [55] and explained in detail in [PERSON] et al. [56]; _CartonPacks_, proposed in [PERSON] et al. [56]; and _ChoppedPlastic_, from [PERSON] et al. [13]. The tests' strategy set up to analyze the algorithm performance is as follows: the number of different DMs chosen for the tests was three. Two sets of tests have been carried out with the three instances, one of them requiring the alternative intervention of each one of the DMs, in such a way that DM1 assessed the generations \(3g+1\); DM2, the generations \(3g+2\); and the DM3, the generations \(3g+3\), with \(g\) taking values from 0 to 32, which led to a total of 99 generations of the algorithm. However, it was not necessary for each of the DMs to evaluate 33 generations, since, from the moment that one individual appears satisfying all of one DM's preferences, the algorithm is configured in a way that only  
Figure 3: Flow diagram of the algorithm with DMs’ intervention. requires this DM's intervention from some generations to some generations, as mentioned before (this strategy will be called _alternative_). The second one (called _sequential_) requires the intervention of the three DMs in a sequential way. Thus, DM1 evaluates the centers as many times as required by the algorithm until it reaches generation 33, DM2 does so from generation 34 to 66, and DM3 from generation 67 to 99. The tests' strategy is configured in this way to determine if the order of intervention of the DMs is significant in the result or not, which could lead to the conclusion that the order of the DMs' intervention could be even random. In the same way, the _alternative_ strategy requires the coordinated intervention of the DMs, which demands the simultaneous presence of all of them, while the _sequential_ one permits the separate intervention of each one of them. Each set of proofs has been repeated five times. The parameters of the algorithm have been configured as shown in Table 1.  
The first problem, _Slaughterhouse_, consists of 12 facilities that have to be set up in a plant of \(30\times 51.14\) m\({}^{2}\) with an aspect ratio per facility limited to 4. The characteristics of this problem are summarized in Table 2 and Figure 4, and the particular preferences of each one of the DMs are shown in Table 3. The reasons that the DMs wish for the two facilities to either be far from each other or close to each other, to be in the perimeter or in the interior part of the plant, can be diverse. In this case, DM1 considers that A (_Stables_) must be in the perimeter as this is part where new animals enter the plant and it needs a connection with the exterior part to unload. Analogously, DM1 wants L (_Byproduct shipping_) and J (_Shipping_) to be in the perimeter for the same reasons. These reasons could be classified together as _process_. By his/her part, DM2 thinks that K (_Offices_) must be far from A (_Stables_). In this case, the reason is likely due to noise and the bad smell. Equally, he/she thinks that K must be far from I (_Compressor room_), due to machinery noise. DM2 thinks too that J (_Shipping_) and L (_Byproduct shipping_) must be far from each other due to marketing reasons. Finally, DM3 considers that B (_Slaughter_) must be far from J (_Shipping_) due to hygiene reasons; that I (_Compressor room_) must be far from A (_Stables_) due to the machinery's noise, which could disturb the animals; and that H (_Boiler room_) must be close to C (_Entrails_) due to process reasons.