The optimization of fuzzy integrated models with stochastic demand and a lead time that can be controlled

In the context of contemporary supply chain management, the unpredictability of demand and lead time has a substantial influence on the optimization of inventory and the efficiency of cost. A fuzzy integrated inventory model is developed in this work. This model takes into account stochastic demand and controlled lead time, with the intention of improving decision-making in contexts that are fraught with uncertainty. In order to address ambiguity in system parameters, the model that has been suggested incorporates fuzzy logic. This ensures that the order quantity, safety stock, and lead time reduction algorithms are optimized in a robust manner. In order to strike a compromise between minimizing costs and maximizing service levels, a hybrid optimization approach that combines fuzzy mathematical programming and stochastic queueing theory is used. The efficiency of the model in adjusting to shifting demand and enhancing supply chain resilience is shown via numerical tests and sensitivity analysis. In the context of sectors dealing with perishable items, medicines, and surroundings with significant demand unpredictability, the results give useful insights.