Algorithmization of managerial decision-making in an organizational system with alternative suppliers based on expert-optimization modeling

The article addresses the development of algorithmic software for the intelligent support of managerial decision-making in an organizational system with alternative supplies, based on expert-optimization modeling. It is shown that the necessary initial information for the management process is determined by the quantitative characteristics of the connections in the interaction between the management center and the objects of the organizational system within the structural model. The problem-oriented focus of expertoptimization modeling, considering the features of the complex system under study, is characterized. It is proposed to form the process of intelligent support for managerial decision-making by algorithmizing the sequence of expert actions and formalized procedures. The first stage of these actions is aimed at structuring the initial information to enable its effective use in expert evaluation and the search for an optimal solution. The expediency of obtaining expert assessments and organizing the search within the requirements of the optimization problem using combined procedures is substantiated. The article considers algorithmic actions for conducting individual and group expert analysis of the initial information with the aim to obtain parameters for boundary and extreme requirements in multi-alternative optimization problems. An algorithmic scheme combining directed randomized search on the set of alternative optimized variables and a genetic algorithm is proposed. This scheme defines the conditions for alternating the search cycles of the iterative optimization process, with a transition – after meeting the stopping condition – to transforming the results into a managerial decision for determining the best suppliers for each object.