During the post-COVID-19 pandemic recovery phase, decision-makers in the manufacturing and retail sectors are confronted with numerous uncertainties. These issues comprise various aspects of operations, including the acquisition of raw materials or components and planning production activities. Therefore, this research aimed to introduce an innovative dynamic hybrid optimization model that combined probabilistic and fuzzy techniques. The model would offer a solution for addressing the challenges posed by uncertain parameters, particularly in the context of post-pandemic scenarios for production planning and inventory management with multiple periods of observation. The model was designed to handle exceptional circumstances such as parameter uncertainties, augmented demand fluctuations, fuzzy variables, and probabilistic factors. The primary objective of the model was to maximize the expected total profit of the operational process. To achieve this aim, an uncertain programming algorithm based on the interior point method was used to compute the optimal decision for the problem at hand. Through the execution of simulations using randomly generated data, the proposed model was thoroughly evaluated and analyzed with six suppliers, three raw part types, three product types, and six periods. All six suppliers were selected to supply raw parts, however, not all suppliers were selected to supply particular raw part types. Furthermore, it was derived that the expectation of the maximum profit is 897261.40; this is the best expected profit generated by the optimization model, meaning that other decisions may result in a smaller expectation of the profit. The results of these simulations unequivocally showed the effectiveness of the decision-making model in providing optimal solutions, specifically in terms of raw material procurement and production planning strategies. Subsequently, this model could serve as a valuable tool for decision-makers operating within the manufacturing and retail industries.
