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Warehousing encompasses all the material handling tasks conducted within the confines of a supply chain's warehouse. Typical warehouse operations include complex allocation decisions that have room for optimization. This paper undertakes optimization models proposed in recent literature for allocating warehouse space. The main objective is to compare previous research on optimizing warehouse space and to elaborate warehouse space optimization using the (LP) linear programming. The model calculates the daily pallet requirements based on daily demand and various warehousing operation constraints in a multi-product manufacturing setting, providing feasible solutions that meet all constraints. The research is expected to serve as a point of reference for future research in this field. The research presents a simple and efficient (LP) linear programming model for optimizing warehouse storage space through effective palletization.Introduction: Warehousing kept all the activities related to material handling that occur within a warehouse.A Highly Effective Method for Organizing Diverse Products in Warehouses using Linear Programming (LPP)'. This groundbreaking strategy enhances warehouse design, optimizing space usage while reducing operational expenses. Utilizing LPP, it allocates goods strategically, considering demand trends and operational limitations. By harmonizing storage needs with operational effectiveness, this method provides a structured approach to bolster warehouse management. Its adoption ensures smoother logistics, heightened inventory management, and increased productivity. This study introduces an innovative framework set to transform warehouse functions, paving the way for more streamlined and proficient supply chain operations. Objectives: Objective: Create a methodical strategy to enhance warehouse layout and storage efficiency by employing innovative methods such as Linear Programming (LPP) to strategically distribute commodities, boosting operational effectiveness, and elevating supply chain productivity. Methods: By applying a Linear Programming (LP) model, optimize the area in the palletizer warehouse with respect to the objective function, Constraints & Decision Variables. Results: The solution is both feasible and falls within the accepted tolerance for integer solutions, indicating that the recommended manufacturing quantities can be practically implemented. The objective function, represented as "Max Z," achieves a maximum value of 29,965 units, which represents the maximum combined manufacturing quantity for all three products & the optimal values for decision variables are presented in Table III. Importantly, all constraints are met and satisfied, ensuring that the optimized solution aligns with the limitations and requirements of the manufacturing process. Conclusions: In summary, utilizing Microsoft Excel alongside the Solver add-in and the Simplex LP algorithm, we've determined the optimal manufacturing quantities for each product type. These quantities are getting in an optimized form. Microsoft Excel's Solver add-in provides a robust platform for resolving mathematical models, empowering users to define objective functions, set constraints, and identify optimal solutions that either maximize or minimize objectives while adhering to specified constraints.
Chhavi Gupta (Thu,) studied this question.