## 4 Discussion
Nonlinearity is also hidden in conditions (5) and (6) because of the normalization. Hence, practically speaking, the problem would fall in the category of a nonlinear and mixed-integer mathematical problem, a potentially very difficult problem to solve [8; 9]. Therefore, the procedure described earlier, although not being as elegant as problem (1)-(6) due to the discretization and need to look for different interesting weights through programmed cycles and conditions, makes more sense for practical purposes. It should be noted that AHP is not the only model that can be applied to describe the decision-making process of the market. Other techniques, perhaps most frequently referenced in literature, include methods of the AGREPEF, ELECTRE, or PROMETHEE type, which are based on sensitivity thresholds [10]. None of the techniques is regarded as the very best, regardless of the problem at hand [11]. Each of the methods has some advantages and disadvantages [12]. Each of them is based on a different principle. This means, unless one has a specific decision problem in mind, that, generally speaking, more techniques can be used to put the ideas presented in this text into practice. The selected technique should not be too complex for companies, however, which is one of the reasons AHP was exploited. Another criterion that determined the method selected was whether the procedure is able to rank variants from best to worst, or whether it can only separate better variants from worse. Taking this factor and the factor of simplicity into consideration, AHP should be preferred to the methods based on sensitivity thresholds because application of the latter is complicated by looking for not only the weights of the decision-making criteria, but also for the sensitivity thresholds, and with PROMETHEE-type methods also for preferential functions. Additionally, in the case of AGREPEF and ELECTRE methods, in their basic form, complete ranking of variants is not guaranteed, and the resulting ranking relation is not even transitive. Another very popular approach in the decision-making modelling is represented by the TOPSIS method. TOPSIS could be potentially used instead of AHP in the presented procedure, i.e., it could be applied to find the various conceivable weights (step 1 of the suggested procedure), and then, based on the weights found, to detect the best product. Despite that, AHP may have several advantages over TOPSIS. TOPSIS, in its original form, does not work with the possibility that decision criteria can be intertwined, as opposed to AHP in its extended network-based form. Some attempts to extend TOPSIS in this sense were already done [13], but the extension is not always applicable (there has to be more variants than criteria when the procedures described in [13] are to be applied). Overall, however, the experience from practice is such that with the same set of weights, the two methods yield more often than not the same rankings of the variants, and so no harm will be done if any of the two methods is used in practice to implement the four steps of the suggested procedure. Generally, an optimal product or an entire product line has been historically sought either by multidimensional scaling methods, which assume that the utility of a product is given by how far it is from an ideal point in the space of product variants, a methodology postulating that product utilities are symmetric around the ideal product point, or by conjoint analysis-based methods which have no specific assumption about the form of utility functions. The approaches work with different data sources. The latter methods derive product utilities either directly from the quantitative nature of product characteristics, or via interviewing a sample of customers (especially when the product design contains qualitative features). The AHP-based approach is not distance-based, and it takes into account the levels of product attributes together with their importances.