Article deals with the problem of technology selection for construction project. Three criteria were proposed: cost, time and technological complexity. To solve the problem, fuzzy preference relations were used. Authors present an algorithm supporting multi-criteria decision-making process. The algorithm creates fuzzy preference relations on the basis of the fuzzy comparison: “xᵢ is better than xj”.Then, with the use of criteria weights it creates general fuzzy preference relation, finds all non-dominated (admissible) alternatives and the best one among them. The algorithm consists of 7 steps. Authors show application of the proposed algorithm – example calculations.

The new approach to the construction project planning is presented in the article. The classical net model is enriched by the fuzzy decision node. The decision node allows for alternate choices dependent on appearing circumstances. The alternative net model with fuzzy decision node is an acyclic multi-graph, where some, chosen nodes (events) have multiple connections. These connections represent alternative methods of the certain work execution. Every work (activity) (i, j) in the net model with alternative methods of work execution, despite the basic information comprising the execution time, the cost, the number of necessary workers, should comprise additional information e.g. about a complexity of works, a real feasibility. The alternative ways of a given work execution are evaluated in the decision node based on the fuzzy decision model. Each method is evaluated by assigning it the preference level in a form of the value of the membership function – „equal or higher” μ_≥. The most preferable way of the work execution in a given circumstances, will have the highest value of preference level. When the choice is done the net model is solved in the traditional way. Therefore, the paper concentrates on the process of choosing the method of work execution in the fuzzy decision node. The example calculations accompanying the process of decision taking are presented too. The model requires the use of linguistic variables, a fuzzy numbers, as well as fuzzy preference relations together with some calculations applied the probability theory.