In the construction practice, at the stage of planning of a construction project, planners are trying to take into account the possibility of unfavorable situations and their consequences during the project execution. Therefore, planning decisions should use appropriate tools for uncertainty modeling as well as consider alternative options for the implementation of the entire undertaking or the most sensitive (critical) works. Taking into account the fact that the management and planning of construction projects is carried out in conditions of non-stochastic uncertainty, the authors propose to create an alternative model with a fuzzy decision node based on classical network models. This approach allows to model activities that are alternative to each other. An important methodological element is the fuzzy assessment of the considered alternatives with the use of fuzzy set theory. The proposed alternative network model with a fuzzy decision node gives the possibility to conduct a comprehensive analysis with the possibility of taking into account and modeling the uncertainty of input data in an easy way. The numerical example shows the possibility of quick selection of the best variant based on the adopted assessment criteria and allows to serialize work variants, which gives additional information in the alternative activities of the network model in the form of preferences. The combination of a decision model in the form of a decision node with a network model can be useful in both strategic and tactical planning of the implementation of construction projects.

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.

During implementation of construction projects, durations of activities are affected by various factors. Because of this, both during the planning phase of the project as well as the construction phase, managers try to estimate, or predict, the length of any delays that may occur. Such estimates allow for the ability to take appropriate action in terms of planning and management during the execution of construction works. This paper presents the use of the non-deterministic concept for describing the uncertainty of estimating works duration. The concept uses the theory of fuzzy sets. The author describes a method for fuzzy estimations of construction works duration based on the fact that uncertain data is an inherent factor in the conditions of construction projects. An example application of the method is presented. The author shows a fuzzy estimation for the duration of an activity, taking into consideration the distorting influence caused by malfunctioning construction equipment and delivery delays of construction materials.

A distinctive feature of construction projects is the uniqueness of conditions for their implementation. Therefore, the durations data of the works are often formulated imprecisely. On the other hand, the time limit for the execution of the works is defined in the contract conditions by data indicating the due time and the deadline. These indicate the distribution of time values, which the contractor can allocate for the implementation of the construction project in accordance with the client preferences. For assessing the compliance of the planned project makespan with the fuzzy time constraint, it is recommended to use the fuzzy sets theory. The article presents a method for assessing the level of satisfaction of fuzzy constraint of time for the execution of construction works using the probabilistic measure, in conjunction with the concept of α-cuts of a fuzzy number. The application of the presented method is demonstrated on a numerical example, which shows the compliance of the evaluation results with those obtained with the simulation methods. The article confirms the correctness of the assumptions of the presented method, which allows for its use for the formulation and resolving schedule optimization problems in the case of imprecisely formulated schedule input data.