Fuzzy non-linear programming for fuzzy inventory model with storage space and budget constraint / Norpah Mahat and Siti Fatimah Amalina Abdul Razak

With an emphasis on improving inventory control for a convenience shop in Malaysia, this study explores the use of a fuzzy inventory model in the context of retail management. Data from Mohd Noor Mart, an actual retail location at UiTM Perlis, is gathered for the study to evaluate the effectiveness of the concept. This study uses fuzzy non-linear programming, which was carefully considered, to address the fuzzy inventory model with storage space and budget constraints. The goals include evaluating the fuzzy method's performance in this situation, figuring out how best to divide the available funds and space among the different products, and figuring out which space is best for the product that is in the greatest demand. The study's scope focuses on a fuzzy inventory model that specifically addresses storage space and budget constraints, which are critical factors in avoiding issues such as overstocking, understocking, and financial strain. The study is significant for businesses looking to improve their inventory management by implementing a fuzzy inventory model. The study's findings indicate that the proposed inventory model and solution method are effective tools for retail managers facing real-world challenges. The methodology used is fuzzy non-linear programming using MATLAB R2023a. The results show that the optimal order quantity is 10,000 units, with a corresponding demand (d*) of 14.142136 and an optimal alpha value of 1. These findings demonstrate the effectiveness of the proposed approach in optimizing inventory management, especially in the face of uncertainty. Ultimately, this study provides valuable insights and a practical solution for businesses looking to improve their inventory management processes, highlighting the applicability and effectiveness of fuzzy non-linear programming in addressing the complexities of inventory models with storage space and budget constraints.