Evaluation of the Effectiveness of Fuzzy Logic Sensitivity Analysis in Matlab Works for Evaluating the EOQ Production Inventory Model with Variable Demand with the Lagrangian and Kuhn-Tucker Methods

Abstract

Abstract: To mirror the real world, the various inventory cost characteristics are also shown as interval numbers. The assumption is that the relationship between the product's demand and selling price is linearly descending. The supply chain has been optimized in this article using these two techniques with random demand. These two approaches have been compared, and a potential machine-learning strategy combining these two approaches has also been offered. The demand is thought to be random. Crisp and fuzzy models were utilized in this study to fix perishable products in the manufacturing process. The proposed model is solved using both the nonlinear mathematical Programming Lagrangian and Kuhn-tucker Methods. The Grade Mean Integration Representation technique is used to defuzzify data in the Fuzzy Inventory Model, which uses a Trapezoidal Fuzzy Number to determine the lowest prices. To support the solution process, a numerical example that includes sensitivity analysis is done at the end. Lagrangian, Kuhn-Tucker, and fuzzy logic analysis are used to analyze the Economic Order Quantity (EOQ) for changeable demand in this study. This research compares and contrasts their approaches, and the findings demonstrate the superiority of fuzzy logic over traditional approaches. To explore the price-dependent coefficients with variable demand and unit purchase cost over variable demand, trapezoidal fuzzy numbers are used in this research. The outcomes closely resemble the clean output. To validate the model, sensitivity analysis in Matlab was additionally carried out