## 3 Methodology
### Rule Base  
The fuzzification stage is followed by the inference stage. It proceeds on the basis of the created rule base in relation to four groups of factors: \(Z_{1}\), \(Z_{2}\), \(Z_{3}\) and \(Z_{4}\), the classification of which is shown in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Organisational** & **Technological** & **Management Factors** & **Design Factors** \\
**Factors (Group \(Z_{1}\))** & **Factors (Group \(Z_{2}\))** & **(Group \(Z_{3}\))** & **(Group \(Z_{4}\))** \\ \hline \hline \multirow{2}{*}{Type of works} & Construction works & \multirow{2}{*}{Project management} & Implementation \\  & technology & & deadline \\ \hline \multirow{2}{*}{Employee qualifications} & Degree of the & Cooperation between & \\  & prefabrication & the contractor and & \\  & of elements & the designer & \\ \hline \multirow{2}{*}{Physical conditions at the site} & Degree of the & \multirow{2}{*}{Availability of the works} & \multirow{2}{*}{Amount of works} \\ \cline{1-1}  & the site & & \\ \end{tabular}
\end{table} 
Table 3: Classification of factors. Figure 2: Membership functions of the types of impact factors. The groups of factors relate to the organisational, technological, management and design aspects of the construction project being implemented. The inference block calculates, on the basis of the input membership degrees, the so-called resulting membership function \(\mu(y)\) of the model's output. To determine the resulting function, the rule base, the inference mechanism (function) and the membership functions of the model's outputs must be defined. The rule base contains logical rules defining cause-effect relationships existing between fuzzy sets of inputs and outputs. The rule base built for the purpose of the developed model consists of 1372 rule variants: 343 variants for each of the 4 groups of factors. The general notation of the basic rule for a single group of factors is as follows:  
_\"IF the impact of factor \(x_{i}\) on planned employment is [large negative/negative/small negative/neutral/small positive/positive/large positive]_  
_AND the type of impact of factor \(x_{i}\) on planned employment is [large negative/negative/small negative/neutral/small positive/positive/large positive]_  
_AND the type of impact of factor \(x_{k}\) on planned employment is [large negative/negative/small negative/neutral/small positive/positive/large positive]_  
_THEN, due to factor group \(Z_{i}\), [decrease a lot/decrease a little/do not change/increase a little/increase a lot] the planned number of man-hours.\"_  
Inference in the Mamdani-Zadeh scheme used leads first to the determination of the expected effect of the influencing factors based on the relationships described in the first set of rules. Then, using the dependencies described in the second set of rules, the effect of this influence is determined, i.e., in this case, the expected change in the planned number of man-hours. For each configuration, it is recommended to change the planned number of man-hours due to the three factors belonging to one of the groups and their weights that are considered in the case. For example, for the recommendation \"increase a lot\" or \"decrease a lot\", the value of the resulting weight of a given factor configuration had to be within the range of the importance indexes of the high weights, i.e., greater than 75 (the lowest value of the importance index for the set of high weights). Seven fuzzy sets were created for the set of possible values of change (decrease or increase) in planned employment: _\"decrease a lot\"_, \"decrease\"_, \"decrease a little\"_, \"_does not change_\", \"_increase a little_\", \"_increase_\" and \"_increase a lot_\". In the next step, the membership functions of the model's outputs were developed. The inference mechanism used is the maximal compound of the fuzzy relations.