Enhanced Optimization of inventory production model: Incorporating fuzzy logic, partial trade credit policy, and reliability factors

One of the most crucial components of both product demand and the production inventory system is reliability. Demand is increased during the manufacturing process by products that are more stable and efficient, but credit is also a firm’s business tactic. Combining these two ideas, we have mathematically evaluated and defined a manufacturing order quantity with a partly impact of credit availability and dependability influence on the supply chain, where the demand from the consumers is influenced by the goods’ prices and the absorption is regarded as a constant. Now consider all conceivable scenarios based on acceptable credit durations, the suggested model introduces credit terms policies for both the supplier and the client. To find the optimal solution, Nonlinear Programming Lagrangian Method is used, which makes an impacts on the Average Monthly Cost. In the proposed model, for fuzzification we use the Trapezoidal Fuzzy Number (TFN) to determine the optimal cost and defuzzification using the novel method called graded mean integration (GMI). In order to evaluate the integrated inventory model, the Python code is used in calculating the economic order quantity (EOQ) and the Total Cost (TC) by generating a CSV file. MATLAB is used to compare the crisp set and the fuzzy set graphically.