articles

When managing complex projects related to the development and organization of production of innovative products, the decision-making process is influenced by many situational aspects. This complicates the assessment of the quality of decisions, which are usually multi-variant and require taking into account random influences. In such cases, a significant effect can be achieved by using bioinspired methods that allow one to find a solution acceptable for a specific situation, in which elements of fuzzy set theory are used to describe ­NON-factors. The article proposes a generalized approach to creating a model based on the specified methods, which is intended to support decision-making in managing an innovative project. This model is distinguished by the comprehensive use of fuzzy bioinspired methods for selecting and justifying options for action in strategic and operational planning and situational management of project activities, taking into account the general and specific characteristics of the project stages, as well as the dynamic nature of external and internal factors. The proposed approach forms the basis of the developed fuzzy method for selecting equipment for conducting experimental design work and organizing the production of innovative products using a model of the behavior of a pack of wolves during hunting. The method is distinguished by the use of a fuzzy Euclidean measure of proximity between the quality indicators of the options being evaluated and the three best ones selected at a given iteration (alpha, beta, and delta solutions) to determine the direction of the search for a rational set of equipment, a modification of the rules for searching for solutions (movement of individuals) based on the consideration of the “depth of matches” and the increment of the effect, including for finding a reasonable balance between directed and random search, and the use of a base of fuzzy production rules when choosing a method for forming the basis for an alpha solution at subsequent iterations. The method is implemented in Python 3.12.0. The effectiveness of the proposed approach is confirmed by data from a computational experiment.