Keywords
multi-criteria optimization
portfolio of managerial decisions
robust optimization
CVaR
parametric uncertainty
How to Cite
Abstract
The article develops a multi-criteria optimization model for constructing a portfolio of managerial decisions aimed at enterprise adaptive development, taking into account resource constraints, economic interdependencies among measures, and parametric uncertainty. The study focuses on substantiating the rational composition of the portfolio, the intensity of implementing alternative measures, and the sequence of their implementation. Economic and mathematical modelling, multi-criteria optimization, stochastic programming, scenario analysis, and risk assessment methods are employed. The Adaptive Development Portfolio Optimization Model (ADPOM) is developed using discrete and continuous decision variables. A matrix of synergistic and conflicting interactions is applied to account for interdependencies among managerial measures. The resilience of portfolio decisions is examined through robust optimization and Conditional Value at Risk (CVaR) assessment. A nonlinear relationship between adaptive portfolio performance and available financing is established. The effects of resource conflicts, time lags, and diminishing marginal returns on the selection of managerial alternatives are identified. The rationale for integrating economic performance with risk control and resource reservation is substantiated. Changes in portfolio configuration under increasing parametric uncertainty are examined. An integrated adaptive portfolio optimization model is proposed, combining economic returns, changes in the enterprise's adaptive state, synergies among managerial measures, tail risk, and the robustness of managerial decisions. A methodological approach to determining the marginal efficiency of alternatives and assessing the value of adaptive optimization is further developed. The proposed analytical framework is designed to support managerial decisions concerning digitalization, production modernization, resource optimization, and logistics processes. Its application enables the comparison of alternative portfolio configurations, alignment of investment priorities with enterprise resource capabilities, and substantiation of implementation schedules. The proposed model also contributes to establishing budget reserves, timely revision of managerial priorities, and strengthening the enterprise's market resilience.
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